COVFEFA_RMarkdown
Daniel_Kim
5/25/2020
- Create Database
#1 Import Packages
#1 Import necessary R packages for project
library(readr)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(tibble)
library(readxl)
library(tidyverse)## ── Attaching packages ───────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.1 ✓ stringr 1.4.0
## ✓ tidyr 1.1.0 ✓ forcats 0.5.0
## ✓ purrr 0.3.4## ── Conflicts ──────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(lubridate)##
## Attaching package: 'lubridate'## The following objects are masked from 'package:dplyr':
##
## intersect, setdiff, union## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, unionlibrary(ggplot2)
library(ggrepel)
library(FactoMineR)## Warning: package 'FactoMineR' was built under R version 4.0.1library(factoextra)## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa#2 updated_base_file
#2.1 This base file includes the country names and the country codes for each country used. This file will be used to accumulate data on the different versions of Country Names that each country could have. Ex.) USA can be represented as 'United States of America" or "United States". Later code will be performing the full_join function based on corresponding alpha-3 Country Codes because it is standardized. Country Names aren't standardized. The base file is also used to add country codes to variable files that don't already have country codes.
updated_base_file <- read_csv("base_file.csv")## Parsed with column specification:
## cols(
## Country_Name = col_character(),
## Country_Code = col_character()
## )#2.2 Fix the weird country names with weired symbols. A name change is required for convenience.
updated_base_file$Country_Name[updated_base_file$Country_Code == 'CUW'] <- 'Curacao'
updated_base_file$Country_Name[updated_base_file$Country_Code == 'CIV'] <- 'Ivory Coast'
updated_base_file$Country_Name[updated_base_file$Country_Code == 'ALA'] <- 'Aland Islands'
updated_base_file$Country_Name[updated_base_file$Country_Code == 'REU'] <- 'Reunion'
updated_base_file$Country_Name[updated_base_file$Country_Code == 'BLM'] <- 'Saint Barthelemy'#3 Population Variable
#3.1 Import the population variable file
population2020 <- read_csv("worldpopreview.csv")## Parsed with column specification:
## cols(
## Rank = col_double(),
## Country_Name = col_character(),
## pop2020 = col_double(),
## pop_2020 = col_double(),
## pop2019 = col_double(),
## GrowthRate = col_double(),
## area = col_double(),
## Density = col_double()
## )#3.2 Population file does not include country codes. Left_join by 'Country_Name' with population file and base_file to obtain country codes for population file. We always need the country codes for each variable file since we will be using country codes for the full_join function later.
population2020 <- left_join(select(population2020, 'Country_Name', 'pop_2020'), updated_base_file, by = 'Country_Name')
#3.3 Check for missing country codes on the population datasheet using the is.na function. Add all list of depicted countries with corresponding country codes into the base_file.
population2020$Country_Name[is.na(population2020$Country_Code)]## [1] "United States" "Russia" "Vietnam"
## [4] "DR Congo" "Iran" "United Kingdom"
## [7] "Tanzania" "South Korea" "Venezuela"
## [10] "North Korea" "Taiwan" "Syria"
## [13] "Bolivia" "Czech Republic" "Hong Kong"
## [16] "Laos" "Republic of the Congo" "Palestine"
## [19] "Moldova" "Macedonia" "Macau"
## [22] "Cape Verde" "Brunei" "Micronesia"
## [25] "Sint Maarten" "Saint Martin" "Wallis and Futuna"
## [28] "Saint Barth\x92\xa9lemy" "Falkland Islands" "Vatican City"updated_base_file <- rbind(updated_base_file, c("United States", "USA"))
updated_base_file <- rbind(updated_base_file, c("Russia", "RUS"))
updated_base_file <- rbind(updated_base_file, c("Vietnam", "VNM"))
updated_base_file <- rbind(updated_base_file, c("DR Congo", "COD"))
updated_base_file <- rbind(updated_base_file, c("Iran", "IRN"))
updated_base_file <- rbind(updated_base_file, c("United Kingdom", "GBR"))
updated_base_file <- rbind(updated_base_file, c("Tanzania", "TZA"))
updated_base_file <- rbind(updated_base_file, c("South Korea", "KOR"))
updated_base_file <- rbind(updated_base_file, c("Venezuela", "VEN"))
updated_base_file <- rbind(updated_base_file, c("North Korea", "PRK"))
updated_base_file <- rbind(updated_base_file, c("Taiwan", "TWN"))
updated_base_file <- rbind(updated_base_file, c("Syria", "SYR"))
updated_base_file <- rbind(updated_base_file, c("Bolivia", "BOL"))
updated_base_file <- rbind(updated_base_file, c("Czech Republic", "CZE"))
updated_base_file <- rbind(updated_base_file, c("Hong Kong", "HKG"))
updated_base_file <- rbind(updated_base_file, c("Laos", "LAO"))
updated_base_file <- rbind(updated_base_file, c("Republic of the Congo", "COG"))
updated_base_file <- rbind(updated_base_file, c("Palestine", "PSE"))
updated_base_file <- rbind(updated_base_file, c("Moldova", "MDA"))
updated_base_file <- rbind(updated_base_file, c("Macedonia", "MKD"))
updated_base_file <- rbind(updated_base_file, c("Macau", "MAC"))
updated_base_file <- rbind(updated_base_file, c("Cape Verde", "CPV"))
updated_base_file <- rbind(updated_base_file, c("Brunei", "BRN"))
updated_base_file <- rbind(updated_base_file, c("Micronesia", "FSM"))
updated_base_file <- rbind(updated_base_file, c("Sint Maarten", "SXM"))
updated_base_file <- rbind(updated_base_file, c("Saint Martin", "MAF"))
updated_base_file <- rbind(updated_base_file, c("Wallis and Futuna", "WLF"))
updated_base_file <- rbind(updated_base_file, c("Falkland Islands", "FLK"))
updated_base_file <- rbind(updated_base_file, c("Vatican City", "VAT"))
#3.4 Will need to do another left_join with the population2020 file and the updated base file that includes the orginally missing country codes
population2020 <- left_join(select(population2020, 'Country_Name', 'pop_2020'), updated_base_file, by = 'Country_Name')
#3.5 Check again which country code is NA in population2020 file
population2020$Country_Name[is.na(population2020$Country_Code)]## [1] "Saint Barth\x92\xa9lemy"#3.6 Unique symbol for Saint Barthélemy requires manual change to add country code
population2020$Country_Code[is.na(population2020$Country_Code)] <- 'BLM'
# 3.7 import all_data file which orginally includes only country names and country codes. Need to change the weird symbols in this file as well.
all_data <- read_csv("base_file.csv")## Parsed with column specification:
## cols(
## Country_Name = col_character(),
## Country_Code = col_character()
## )all_data$Country_Name[all_data$Country_Code == 'CUW'] <- 'Curacao'
all_data$Country_Name[all_data$Country_Code == 'CIV'] <- 'Ivory Coast'
all_data$Country_Name[all_data$Country_Code == 'ALA'] <- 'Aland Islands'
all_data$Country_Name[all_data$Country_Code == 'REU'] <- 'Reunion'
all_data$Country_Name[all_data$Country_Code == 'BLM'] <- 'Saint Barthelemy'
#3.8 Since we have the necessary country codes for population2020 now, we can use full_join to merge the population column into the all_data spreadsheet (now Taiwan is added to all_data)
all_data <- full_join(all_data, select(population2020, 'pop_2020', 'Country_Code'), by = 'Country_Code')
#3.9 Need to manually input the Country Name Taiwan to all_data
all_data$Country_Name[all_data$Country_Code == 'TWN'] <- 'Taiwan'
#3.10 Use Codes to test a few cases
population2020 %>% filter(Country_Code == 'CHN') %>% select(pop_2020) == all_data %>% filter(Country_Code == 'CHN') %>% select(pop_2020)## pop_2020
## [1,] TRUEpopulation2020 %>% filter(Country_Code == 'USA') %>% select(pop_2020) == all_data %>% filter(Country_Code == 'USA') %>% select(pop_2020)## pop_2020
## [1,] TRUEpopulation2020 %>% filter(Country_Code == 'ARG') %>% select(pop_2020) == all_data %>% filter(Country_Code == 'ARG') %>% select(pop_2020)## pop_2020
## [1,] TRUEpopulation2020 %>% filter(Country_Code == 'NGA') %>% select(pop_2020) == all_data %>% filter(Country_Code == 'NGA') %>% select(pop_2020)## pop_2020
## [1,] TRUEpopulation2020 %>% filter(Country_Code == 'BEL') %>% select(pop_2020) == all_data %>% filter(Country_Code == 'BEL') %>% select(pop_2020)## pop_2020
## [1,] TRUE#4 GDP Variable
#4.1 Import the GDP file
GDP_2018 <- read_csv("API_NY.GDP.MKTP.CD_DS2_en_csv_v2_936013.csv",
skip = 4)## Parsed with column specification:
## cols(
## .default = col_double(),
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Indicator Name` = col_character(),
## `Indicator Code` = col_character(),
## `2019` = col_logical()
## )## See spec(...) for full column specifications.#4.2 Change the column name of 'Country Code' to 'Country_Code' in GDP_2018 in order to match column names when using full_join
colnames(GDP_2018)[which(names(GDP_2018) == "Country Code")] <- "Country_Code"
#4.3 Change the column name of '2018' to 'GDP_2018' in GDP_2018
colnames(GDP_2018)[which(names(GDP_2018) == "2018")] <- "GDP_2018"
#4.4 Use the full_join function to merge the GDP column variable onto all_data
all_data <- full_join(all_data, select(GDP_2018, 'Country_Code', 'GDP_2018'), by = 'Country_Code')
#4.5 Add the Country Name for Kosovo into the all_data sheet
all_data$Country_Name[all_data$Country_Code == 'XKX'] <- 'Kosovo'
#4.6 The GDP file includes geographic regions as well that are not countries. These rows are also added to all_data. Best to filter out these rows.
all_data <- all_data %>% filter(!is.na(Country_Name))
#4.7 Taiwan GDP retrieved data from https://countryeconomy.com/gdp/taiwan#:~:text=The%20GDP%20figure%20in%202018,2017%2C%20when%20it%20was%20%2424%2C390.
all_data$GDP_2018[all_data$Country_Code == "TWN"] = 589906000000
#4.8 Add a few test cases
GDP_2018 %>% filter(Country_Code == 'USA') %>% select(GDP_2018) == all_data %>% filter(Country_Code == 'USA') %>% select(GDP_2018)## GDP_2018
## [1,] TRUEGDP_2018 %>% filter(Country_Code == 'URY') %>% select(GDP_2018) == all_data %>% filter(Country_Code == 'URY') %>% select(GDP_2018)## GDP_2018
## [1,] TRUEGDP_2018 %>% filter(Country_Code == 'TZA') %>% select(GDP_2018) == all_data %>% filter(Country_Code == 'TZA') %>% select(GDP_2018)## GDP_2018
## [1,] TRUEGDP_2018 %>% filter(Country_Code == 'UGA') %>% select(GDP_2018) == all_data %>% filter(Country_Code == 'UGA') %>% select(GDP_2018)## GDP_2018
## [1,] TRUEGDP_2018 %>% filter(Country_Code == 'ESP') %>% select(GDP_2018) == all_data %>% filter(Country_Code == 'ESP') %>% select(GDP_2018)## GDP_2018
## [1,] TRUEGDP_2018 %>% filter(Country_Code == 'SRB') %>% select(GDP_2018) == all_data %>% filter(Country_Code == 'SRB') %>% select(GDP_2018)## GDP_2018
## [1,] TRUEGDP_2018 %>% filter(Country_Code == 'PHL') %>% select(GDP_2018) == all_data %>% filter(Country_Code == 'PHL') %>% select(GDP_2018)## GDP_2018
## [1,] TRUE#5 Median Age
#5.1 Import the Median Age file and get rid of Gaza Strip PSE/change BUR to MMR
MedianAge_2020 <- read_excel("Median_Age_Final.xlsx")
colnames(MedianAge_2020)[which(names(MedianAge_2020) == "Country Name")] <- "Country_Name"
MedianAge_2020 <- MedianAge_2020 %>% filter(Country_Name != 'Gaza Strip')
MedianAge_2020$Country_Code[MedianAge_2020$Country_Code == 'BUR'] <- 'MMR'
#5.2 Use the full_join function to merge the median age variable column to the all_data spreadsheet
all_data <- full_join(all_data, select(MedianAge_2020, 'Country_Code', 'Median_Age'), by = 'Country_Code')
#5.3 Change the column name of median age to the appropriate column name in all_data
colnames(all_data)[which(names(all_data) == "Median_Age")] <- "MedianAge_2020"
#5.4 Run a few test cases
MedianAge_2020 %>% filter(Country_Code == 'JPN') %>% select(Median_Age) == all_data %>% filter(Country_Code == 'JPN') %>% select(MedianAge_2020)## Median_Age
## [1,] TRUEMedianAge_2020 %>% filter(Country_Code == 'PRT') %>% select(Median_Age) == all_data %>% filter(Country_Code == 'PRT') %>% select(MedianAge_2020)## Median_Age
## [1,] TRUEMedianAge_2020 %>% filter(Country_Code == 'AUT') %>% select(Median_Age) == all_data %>% filter(Country_Code == 'AUT') %>% select(MedianAge_2020)## Median_Age
## [1,] TRUEMedianAge_2020 %>% filter(Country_Code == 'EST') %>% select(Median_Age) == all_data %>% filter(Country_Code == 'EST') %>% select(MedianAge_2020)## Median_Age
## [1,] TRUEMedianAge_2020 %>% filter(Country_Code == 'CZE') %>% select(Median_Age) == all_data %>% filter(Country_Code == 'CZE') %>% select(MedianAge_2020)## Median_Age
## [1,] TRUEMedianAge_2020 %>% filter(Country_Code == 'NLD') %>% select(Median_Age) == all_data %>% filter(Country_Code == 'NLD') %>% select(MedianAge_2020)## Median_Age
## [1,] TRUEMedianAge_2020 %>% filter(Country_Code == 'CAN') %>% select(Median_Age) == all_data %>% filter(Country_Code == 'CAN') %>% select(MedianAge_2020)## Median_Age
## [1,] TRUEMedianAge_2020 %>% filter(Country_Code == 'RUS') %>% select(Median_Age) == all_data %>% filter(Country_Code == 'RUS') %>% select(MedianAge_2020)## Median_Age
## [1,] TRUE#6 Smoking Prevalence
#6.1 Import the smoking prevalence file
smoking_prevalence_2016 <- read_csv("smoking_prevalence_final.csv",
skip = 4)## Parsed with column specification:
## cols(
## .default = col_logical(),
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Indicator Name` = col_character(),
## `Indicator Code` = col_character(),
## `2000` = col_double(),
## `2005` = col_double(),
## `2010` = col_double(),
## `2011` = col_double(),
## `2012` = col_double(),
## `2013` = col_double(),
## `2014` = col_double(),
## `2015` = col_double(),
## `2016` = col_double()
## )## See spec(...) for full column specifications.#6.2 Change the column name of 'Country Code' into 'Country_Code' to match all_data set
colnames(smoking_prevalence_2016)[which(names(smoking_prevalence_2016) == "Country Code")] <- "Country_Code"
#6.3 Use the full_join function to merge the smoking prevalence variable column to the all_data spreadsheet
all_data <- full_join(all_data, select(smoking_prevalence_2016, 'Country_Code', '2016'), by = 'Country_Code')
#6.4 Change the name of '2016' in all_data to 'smoking_prevalence_2016'
colnames(all_data)[which(names(all_data) == "2016")] <- "smoking_prevalence_2016"
#6.5 The GDP file includes geographic regions as well that are not countries. These rows are also added to all_data. Best to filter out these rows.
all_data <- all_data %>% filter(!is.na(Country_Name))
#6.6 HKG 2017 smoking prevalence data from https://www.smokefree.hk/en/content/web.do?page=SmokingTrend
all_data$smoking_prevalence_2016[all_data$Country_Code == 'HKG'] = 10.8
#6.7 Add some test cases
smoking_prevalence_2016 %>% filter(Country_Code == 'YEM') %>% select('2016') == all_data %>% filter(Country_Code == 'YEM') %>% select(smoking_prevalence_2016)## 2016
## [1,] TRUEsmoking_prevalence_2016 %>% filter(Country_Code == 'ZMB') %>% select('2016') == all_data %>% filter(Country_Code == 'ZMB') %>% select(smoking_prevalence_2016)## 2016
## [1,] TRUEsmoking_prevalence_2016 %>% filter(Country_Code == 'ZWE') %>% select('2016') == all_data %>% filter(Country_Code == 'ZWE') %>% select(smoking_prevalence_2016)## 2016
## [1,] TRUEsmoking_prevalence_2016 %>% filter(Country_Code == 'VNM') %>% select('2016') == all_data %>% filter(Country_Code == 'VNM') %>% select(smoking_prevalence_2016)## 2016
## [1,] TRUEsmoking_prevalence_2016 %>% filter(Country_Code == 'USA') %>% select('2016') == all_data %>% filter(Country_Code == 'USA') %>% select(smoking_prevalence_2016)## 2016
## [1,] TRUEsmoking_prevalence_2016 %>% filter(Country_Code == 'TZA') %>% select('2016') == all_data %>% filter(Country_Code == 'TZA') %>% select(smoking_prevalence_2016)## 2016
## [1,] TRUEsmoking_prevalence_2016 %>% filter(Country_Code == 'UKR') %>% select('2016') == all_data %>% filter(Country_Code == 'UKR') %>% select(smoking_prevalence_2016)## 2016
## [1,] TRUEsmoking_prevalence_2016 %>% filter(Country_Code == 'TUN') %>% select('2016') == all_data %>% filter(Country_Code == 'TUN') %>% select(smoking_prevalence_2016)## 2016
## [1,] TRUE#7 Freedom Index (Source definition: Freedom index is the average of political rights and civil liberties ratings and is used to determine countries’ freedom statuses. It’s range on a scale of 1(most free) to 7(least free))
#7.1 Import freedom index file
freeidx_2018 <- read_csv("freedix_fh.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## country = col_character(),
## `1982` = col_logical()
## )## See spec(...) for full column specifications.#7.2 Change the column name 'country' to 'Country_Name' in freeidx_2018 file
colnames(freeidx_2018)[which(names(freeidx_2018) == "country")] <- "Country_Name"
#7.3 Change the column name '2018' to 'freedix_2018' in the file freeidx_2018
colnames(freeidx_2018)[which(names(freeidx_2018) == '2018')] <- "freeidx_2018"
#7.4 Freedom Index file does not include country codes. Left_join by 'Country_Name' with freeidx file and base_file to obtain country codes for freeidx file
freeidx_2018 <- left_join(select(freeidx_2018, 'Country_Name', 'freeidx_2018'), updated_base_file, by = 'Country_Name')
#7.5 Check for missing country codes on the freeidx datasheet using the is.na function. Add all list of depicted countries with corresponding country codes into the base_file.
freeidx_2018$Country_Name[is.na(freeidx_2018$Country_Code)]## [1] "Congo, Dem. Rep." "Congo, Rep."
## [3] "Cote d'Ivoire" "Kyrgyz Republic"
## [5] "Lao" "Micronesia, Fed. Sts."
## [7] "North Macedonia" "Slovak Republic"
## [9] "St. Kitts and Nevis" "St. Lucia"
## [11] "St. Vincent and the Grenadines"updated_base_file <- rbind(updated_base_file, c("Congo, Dem. Rep.", "COD"))
updated_base_file <- rbind(updated_base_file, c("Congo, Rep.", "COG"))
updated_base_file <- rbind(updated_base_file, c("Kyrgyz Republic", "KGZ"))
updated_base_file <- rbind(updated_base_file, c("Lao", "LAO"))
updated_base_file <- rbind(updated_base_file, c("Micronesia, Fed. Sts.", "FSM"))
updated_base_file <- rbind(updated_base_file, c("North Macedonia", "MKD"))
updated_base_file <- rbind(updated_base_file, c("Slovak Republic", "SVK"))
updated_base_file <- rbind(updated_base_file, c("St. Kitts and Nevis", "KNA"))
updated_base_file <- rbind(updated_base_file, c("St. Lucia", "LCA"))
updated_base_file <- rbind(updated_base_file, c("St. Vincent and the Grenadines", "VCT"))
#7.6 Will need to do another left_join with the freeidx_2018 file and the updated base file that includes the orginally missing country codes
freeidx_2018 <- left_join(select(freeidx_2018, 'Country_Name', 'freeidx_2018'), updated_base_file, by = 'Country_Name')
#7.7 Check again which country code is NA in population2020 file
freeidx_2018$Country_Name[is.na(freeidx_2018$Country_Code)]## [1] "Cote d'Ivoire"#7.8 Manually input country code for cote d'ivoire
freeidx_2018$Country_Code[is.na(freeidx_2018$Country_Code)] <- 'CIV'
#7.9 Use the full_join function to merge the freedom index variable into the all_data sheet
all_data <- full_join(all_data, select(freeidx_2018, 'Country_Code', 'freeidx_2018'), by = 'Country_Code')
#7.10 Add a few test cases
freeidx_2018 %>% filter(Country_Code == 'AFG') %>% select(freeidx_2018) == all_data %>% filter(Country_Code == 'AFG') %>% select(freeidx_2018)## freeidx_2018
## [1,] TRUEfreeidx_2018 %>% filter(Country_Code == 'ALB') %>% select(freeidx_2018) == all_data %>% filter(Country_Code == 'ALB') %>% select(freeidx_2018)## freeidx_2018
## [1,] TRUEfreeidx_2018 %>% filter(Country_Code == 'ARG') %>% select(freeidx_2018) == all_data %>% filter(Country_Code == 'ARG') %>% select(freeidx_2018)## freeidx_2018
## [1,] TRUEfreeidx_2018 %>% filter(Country_Code == 'BGR') %>% select(freeidx_2018) == all_data %>% filter(Country_Code == 'BGR') %>% select(freeidx_2018)## freeidx_2018
## [1,] TRUEfreeidx_2018 %>% filter(Country_Code == 'CHL') %>% select(freeidx_2018) == all_data %>% filter(Country_Code == 'CHL') %>% select(freeidx_2018)## freeidx_2018
## [1,] TRUEfreeidx_2018 %>% filter(Country_Code == 'BGR') %>% select(freeidx_2018) == all_data %>% filter(Country_Code == 'BGR') %>% select(freeidx_2018)## freeidx_2018
## [1,] TRUEfreeidx_2018 %>% filter(Country_Code == 'COG') %>% select(freeidx_2018) == all_data %>% filter(Country_Code == 'COG') %>% select(freeidx_2018)## freeidx_2018
## [1,] TRUEfreeidx_2018 %>% filter(Country_Code == 'EGY') %>% select(freeidx_2018) == all_data %>% filter(Country_Code == 'EGY') %>% select(freeidx_2018)## freeidx_2018
## [1,] TRUE8 Female/Male Proportions By Country
#Note: Will import both female and male proportions which will be used to calculate the female/male ratio
#8.1 Import female file
female_prop_2018 <- read_csv("female_population.csv",
skip = 4)## Parsed with column specification:
## cols(
## .default = col_double(),
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Indicator Name` = col_character(),
## `Indicator Code` = col_character(),
## `2019` = col_logical()
## )## See spec(...) for full column specifications.#8.2 Change the column name 'Country Code' to 'Country_Code' in female_prop_2018 file
colnames(female_prop_2018)[which(names(female_prop_2018) == "Country Code")] <- "Country_Code"
#8.3 Change the column name '2018' to 'female_prop_2018' in the file female_prop_2018 file
colnames(female_prop_2018)[which(names(female_prop_2018) == '2018')] <- "female_prop_2018"
#8.4 Use the full_join function to merge the female column to the all_data spreadsheet
all_data <- full_join(all_data, select(female_prop_2018, 'Country_Code', 'female_prop_2018'), by = 'Country_Code')
#8.5 The female_pop_2018 file includes geographic regions as well that are not countries. These rows are also added to all_data. Best to filter out these rows.
all_data <- all_data %>% filter(!is.na(Country_Name))
#8.6 Add some test cases
female_prop_2018 %>% filter(Country_Code == 'KIR') %>% select('female_prop_2018') == all_data %>% filter(Country_Code == 'KIR') %>% select(female_prop_2018)## female_prop_2018
## [1,] TRUEfemale_prop_2018 %>% filter(Country_Code == 'JOR') %>% select('female_prop_2018') == all_data %>% filter(Country_Code == 'JOR') %>% select(female_prop_2018)## female_prop_2018
## [1,] TRUEfemale_prop_2018 %>% filter(Country_Code == 'JPN') %>% select('female_prop_2018') == all_data %>% filter(Country_Code == 'JPN') %>% select(female_prop_2018)## female_prop_2018
## [1,] TRUEfemale_prop_2018 %>% filter(Country_Code == 'LAO') %>% select('female_prop_2018') == all_data %>% filter(Country_Code == 'LAO') %>% select(female_prop_2018)## female_prop_2018
## [1,] TRUEfemale_prop_2018 %>% filter(Country_Code == 'LVA') %>% select('female_prop_2018') == all_data %>% filter(Country_Code == 'LVA') %>% select(female_prop_2018)## female_prop_2018
## [1,] TRUEfemale_prop_2018 %>% filter(Country_Code == 'BGD') %>% select('female_prop_2018') == all_data %>% filter(Country_Code == 'BGD') %>% select(female_prop_2018)## female_prop_2018
## [1,] TRUEfemale_prop_2018 %>% filter(Country_Code == 'AUT') %>% select('female_prop_2018') == all_data %>% filter(Country_Code == 'AUT') %>% select(female_prop_2018)## female_prop_2018
## [1,] TRUEfemale_prop_2018 %>% filter(Country_Code == 'ALB') %>% select('female_prop_2018') == all_data %>% filter(Country_Code == 'ALB') %>% select(female_prop_2018)## female_prop_2018
## [1,] TRUE#8.7 Import male file
male_prop_2018 <- read_csv("male_population.csv", skip = 4)## Parsed with column specification:
## cols(
## .default = col_double(),
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Indicator Name` = col_character(),
## `Indicator Code` = col_character(),
## `2019` = col_logical()
## )
## See spec(...) for full column specifications.#8.8 Change the column name 'Country Code' to 'Country_Code' in male_prop_2018 file
colnames(male_prop_2018)[which(names(male_prop_2018) == "Country Code")] <- "Country_Code"
#8.9 Change the column name '2018' to 'male_prop_2018' in the file male_prop_2018 file
colnames(male_prop_2018)[which(names(male_prop_2018) == '2018')] <- "male_prop_2018"
#8.10 Use the full_join function to merge the male column to the all_data spreadsheet
all_data <- full_join(all_data, select(male_prop_2018, 'Country_Code', 'male_prop_2018'), by = 'Country_Code')
#8.11 The male_pop_2018 file includes geographic regions as well that are not countries. These rows are also added to all_data. Best to filter out these rows.
all_data <- all_data %>% filter(!is.na(Country_Name))
#8.12 Add some test cases
male_prop_2018 %>% filter(Country_Code == 'KIR') %>% select('male_prop_2018') == all_data %>% filter(Country_Code == 'KIR') %>% select(male_prop_2018)## male_prop_2018
## [1,] TRUEmale_prop_2018 %>% filter(Country_Code == 'JOR') %>% select('male_prop_2018') == all_data %>% filter(Country_Code == 'JOR') %>% select(male_prop_2018)## male_prop_2018
## [1,] TRUEmale_prop_2018 %>% filter(Country_Code == 'JPN') %>% select('male_prop_2018') == all_data %>% filter(Country_Code == 'JPN') %>% select(male_prop_2018)## male_prop_2018
## [1,] TRUEmale_prop_2018 %>% filter(Country_Code == 'LAO') %>% select('male_prop_2018') == all_data %>% filter(Country_Code == 'LAO') %>% select(male_prop_2018)## male_prop_2018
## [1,] TRUEmale_prop_2018 %>% filter(Country_Code == 'LVA') %>% select('male_prop_2018') == all_data %>% filter(Country_Code == 'LVA') %>% select(male_prop_2018)## male_prop_2018
## [1,] TRUEmale_prop_2018 %>% filter(Country_Code == 'BGD') %>% select('male_prop_2018') == all_data %>% filter(Country_Code == 'BGD') %>% select(male_prop_2018)## male_prop_2018
## [1,] TRUEmale_prop_2018 %>% filter(Country_Code == 'AUT') %>% select('male_prop_2018') == all_data %>% filter(Country_Code == 'AUT') %>% select(male_prop_2018)## male_prop_2018
## [1,] TRUEmale_prop_2018 %>% filter(Country_Code == 'ALB') %>% select('male_prop_2018') == all_data %>% filter(Country_Code == 'ALB') %>% select(male_prop_2018)## male_prop_2018
## [1,] TRUE#9 Total Number of Tests By Country (Will need periodic downloads from the website: https://www.finddx.org/covid-19/test-tracker/)
#9.1 import tests_per_inhab_update file
#sum_tests_latest <- read_csv("update_FIND_COVID_tests.csv")
#new file
sum_tests_latest <- read_csv("new_COVID-19 cases and tests tracker.csv")## Parsed with column specification:
## cols(
## alpha3 = col_character(),
## country = col_character(),
## date = col_date(format = ""),
## new_tests = col_double(),
## tests_cumulative = col_double(),
## population = col_double(),
## testsPer100k = col_double(),
## source = col_character(),
## Income.group = col_character(),
## Continent = col_character(),
## Region = col_character()
## )#9.2 Change column variable name of 'country' into 'Country_Name'
colnames(sum_tests_latest)[which(names(sum_tests_latest) == "country")] <- "Country_Name"
#9.3 Select only the columns that are needed for use
sum_tests_latest <- select(sum_tests_latest, 'Country_Name', 'new_tests')
#9.4 Group_by the country name and find the sum of total cases
sum_tests_latest <- sum_tests_latest %>% group_by(Country_Name) %>% summarise(sum_tests = sum(new_tests, na.rm = TRUE))
#9.5 sum_cases_latest file does not include country codes. Left_join by 'Country_Name' with sum_cases_latest file and base_file to obtain country codes for sum_cases_latest file
sum_tests_latest <- left_join(select(sum_tests_latest, 'Country_Name', 'sum_tests'), updated_base_file, by = 'Country_Name')
#9.6 Check for missing country codes on the sum_cases_latest datasheet using the is.na function. Add all list of depicted countries with corresponding country codes into the base_file.
sum_tests_latest$Country_Name[is.na(sum_tests_latest$Country_Code)]## [1] "Cote d'Ivoire" "Eswatini"
## [3] "Guinea Bissau" "Kosovo"
## [5] "Mainland China" "Occupied Palestinian Territory"
## [7] "The Bahamas" "The Gambia"
## [9] "UK" "USA"updated_base_file <- rbind(updated_base_file, c("Cote d'Ivoire", "CIV"))
updated_base_file <- rbind(updated_base_file, c("Eswatini", "SWZ"))
updated_base_file <- rbind(updated_base_file, c("Guinea Bissau", "GNB"))
updated_base_file <- rbind(updated_base_file, c("Kosovo", "XKX"))
updated_base_file <- rbind(updated_base_file, c("Mainland China", "CHN"))
updated_base_file <- rbind(updated_base_file, c("Occupied Palestinian Territory", "PSE"))
updated_base_file <- rbind(updated_base_file, c("The Bahamas", "BHS"))
updated_base_file <- rbind(updated_base_file, c("The Gambia", "GMB"))
updated_base_file <- rbind(updated_base_file, c("UK", "GBR"))
updated_base_file <- rbind(updated_base_file, c("USA", "USA"))
#9.7 Will need to do another left_join with the sum_cases_latest file and the updated base file that includes the orginally missing country codes
sum_tests_latest <- left_join(select(sum_tests_latest, 'Country_Name', 'sum_tests'), updated_base_file, by = 'Country_Name')
#9.8 Check again which country code is NA in population2020 file
sum_tests_latest$Country_Name[is.na(sum_tests_latest$Country_Code)]## character(0)#9.9 Use the full_join function to merge the sum_cases_latest variable into the all_data sheet
all_data <- full_join(all_data, select(sum_tests_latest, 'Country_Code', 'sum_tests'), by = 'Country_Code')
#9.10 Add a few test cases
sum_tests_latest %>% filter(Country_Code == 'AFG') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'AFG') %>% select('sum_tests')## sum_tests
## [1,] TRUEsum_tests_latest %>% filter(Country_Code == 'ALB') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'ALB') %>% select('sum_tests')## sum_tests
## [1,] TRUEsum_tests_latest %>% filter(Country_Code == 'AZE') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'AZE') %>% select('sum_tests')## sum_tests
## [1,] TRUEsum_tests_latest %>% filter(Country_Code == 'BWA') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'BWA') %>% select('sum_tests')## sum_tests
## [1,] TRUEsum_tests_latest %>% filter(Country_Code == 'CAN') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'CAN') %>% select('sum_tests')## sum_tests
## [1,] TRUEsum_tests_latest %>% filter(Country_Code == 'HKG') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'HKG') %>% select('sum_tests')## sum_tests
## [1,] TRUEsum_tests_latest %>% filter(Country_Code == 'HUN') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'HUN') %>% select('sum_tests')## sum_tests
## [1,] TRUEsum_tests_latest %>% filter(Country_Code == 'ESP') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'ESP') %>% select('sum_tests')## sum_tests
## [1,] TRUEsum_tests_latest %>% filter(Country_Code == 'TUR') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'TUR') %>% select('sum_tests')## sum_tests
## [1,] TRUEsum_tests_latest %>% filter(Country_Code == 'ZWE') %>% select('sum_tests') == all_data %>% filter(Country_Code == 'ZWE') %>% select('sum_tests')## sum_tests
## [1,] TRUE#10 A) Maximum Stringency Index (Needs to be periodically updated from website: https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker)
#10 A.1 Import the Stringency Index File
max_stringency_index <- read_csv("covid-stringency-index-8-6.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Date = col_character(),
## `Government Response Stringency Index ((0 to 100, 100 = strictest))` = col_double()
## )#10 A.2 Change the column name 'Code' into 'Country_Code' and also the stringency index name to simpler form
colnames(max_stringency_index)[which(names(max_stringency_index) == "Code")] <- "Country_Code"
colnames(max_stringency_index)[which(names(max_stringency_index) == "Government Response Stringency Index ((0 to 100, 100 = strictest))")] <- "stringency_index"
#10 A.3 Find the maximum stringency index of each country using group_by and summarise/correct Kosovo
max_stringency_index <- max_stringency_index %>% group_by(Country_Code) %>% summarise(max_stringency_index = max(stringency_index, na.rm = TRUE))
max_stringency_index$Country_Code[max_stringency_index$Country_Code == 'OWID_KOS'] <- 'XKX'
#10 A.4 Use the full_join function to merge the max_stringency_index into the all_data variable
all_data <- full_join(all_data, max_stringency_index, by = 'Country_Code')
#10 A.5 Add a few test cases
max_stringency_index %>% filter(Country_Code == 'ARG') %>% select('max_stringency_index') == all_data %>% filter(Country_Code == 'ARG') %>% select('max_stringency_index')## max_stringency_index
## [1,] TRUEmax_stringency_index %>% filter(Country_Code == 'USA') %>% select('max_stringency_index') == all_data %>% filter(Country_Code == 'USA') %>% select('max_stringency_index')## max_stringency_index
## [1,] TRUEmax_stringency_index %>% filter(Country_Code == 'CHN') %>% select('max_stringency_index') == all_data %>% filter(Country_Code == 'CHN') %>% select('max_stringency_index')## max_stringency_index
## [1,] TRUEmax_stringency_index %>% filter(Country_Code == 'BLZ') %>% select('max_stringency_index') == all_data %>% filter(Country_Code == 'BLZ') %>% select('max_stringency_index')## max_stringency_index
## [1,] TRUEmax_stringency_index %>% filter(Country_Code == 'TWN') %>% select('max_stringency_index') == all_data %>% filter(Country_Code == 'TWN') %>% select('max_stringency_index')## max_stringency_index
## [1,] TRUE#10 B) Medium Stringency Index (Needs to be periodically updated from website: https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker)
#10 B.1 Import the Stringency Index File
median_stringency_index <- read_csv("covid-stringency-index-8-6.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Date = col_character(),
## `Government Response Stringency Index ((0 to 100, 100 = strictest))` = col_double()
## )#10 B.2 Change the column name 'Code' into 'Country_Code' and also the stringency index name to simpler form
colnames(median_stringency_index)[which(names(median_stringency_index) == "Code")] <- "Country_Code"
colnames(median_stringency_index)[which(names(median_stringency_index) == "Government Response Stringency Index ((0 to 100, 100 = strictest))")] <- "stringency_index"
#10 B.3 Find the median stringency index of each country using group_by and summarise/correct kosovo
median_stringency_index <- median_stringency_index %>% group_by(Country_Code) %>% summarise(median_stringency_index = median(stringency_index, na.rm = TRUE))
median_stringency_index$Country_Code[median_stringency_index$Country_Code == 'OWID_KOS'] <- 'XKX'
#10 A.4 Use the full_join function to merge the max_stringency_index into the all_data variable
all_data <- full_join(all_data, median_stringency_index, by = 'Country_Code')
#10 A.5 Add a few test cases
median_stringency_index %>% filter(Country_Code == 'AFG') %>% select('median_stringency_index') == all_data %>% filter(Country_Code == 'AFG') %>% select('median_stringency_index')## median_stringency_index
## [1,] TRUE median_stringency_index %>% filter(Country_Code == 'THA') %>% select('median_stringency_index') == all_data %>% filter(Country_Code == 'THA') %>% select('median_stringency_index')## median_stringency_index
## [1,] TRUE median_stringency_index %>% filter(Country_Code == 'TLS') %>% select('median_stringency_index') == all_data %>% filter(Country_Code == 'TLS') %>% select('median_stringency_index')## median_stringency_index
## [1,] TRUE median_stringency_index %>% filter(Country_Code == 'USA') %>% select('median_stringency_index') == all_data %>% filter(Country_Code == 'USA') %>% select('median_stringency_index')## median_stringency_index
## [1,] TRUE median_stringency_index %>% filter(Country_Code == 'CAN') %>% select('median_stringency_index') == all_data %>% filter(Country_Code == 'CAN') %>% select('median_stringency_index')## median_stringency_index
## [1,] TRUE median_stringency_index %>% filter(Country_Code == 'GBR') %>% select('median_stringency_index') == all_data %>% filter(Country_Code == 'GBR') %>% select('median_stringency_index')## median_stringency_index
## [1,] TRUE#10 C) Earliest Date of Max Stringency Index (Needs to be periodically updated from website: https://www.bsg.ox.ac.uk/research/research-projects/coronavirus-government-response-tracker)
#10 C.1 Import the earliest max stringency file
earliest_max_string_date <- read_csv("covid-stringency-index-8-6.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Date = col_character(),
## `Government Response Stringency Index ((0 to 100, 100 = strictest))` = col_double()
## )#10 C.2 Change the column name 'Code' into 'Country_Code' and also the stringency index name to simpler form
colnames(earliest_max_string_date)[which(names(earliest_max_string_date) == "Code")] <- "Country_Code"
colnames(earliest_max_string_date)[which(names(earliest_max_string_date) == "Government Response Stringency Index ((0 to 100, 100 = strictest))")] <- "stringency_index"
#10 C.3 Find the earliest date of the max stringency index of each country using group_by and summarise/fix kosovo
earliest_max_string_date <- earliest_max_string_date %>% group_by(Country_Code) %>% filter(stringency_index == max(stringency_index, na.rm = TRUE)) %>% slice(1)
earliest_max_string_date$Country_Code[earliest_max_string_date$Country_Code == 'OWID_KOS'] <- 'XKX'
#10 C.4 Change the column name of 'Date' into earliest_max_string_date
colnames(earliest_max_string_date)[which(names(earliest_max_string_date) == "Date")] <- "earliest_max_string_date"
#10 C.5 Use the full_join function to add the earliest_max_string_date variable into the all_data sheet
all_data <- full_join(all_data, select(earliest_max_string_date, 'Country_Code', 'earliest_max_string_date'), by = 'Country_Code')
#10 C.6 Add a few test cases
earliest_max_string_date %>% filter(Country_Code == 'AFG') %>% ungroup() %>% select('earliest_max_string_date') == all_data %>% filter(Country_Code == 'AFG') %>% select('earliest_max_string_date')## earliest_max_string_date
## [1,] TRUEearliest_max_string_date %>% filter(Country_Code == 'ABW') %>% ungroup() %>% select('earliest_max_string_date') == all_data %>% filter(Country_Code == 'ABW') %>% select('earliest_max_string_date')## earliest_max_string_date
## [1,] TRUEearliest_max_string_date %>% filter(Country_Code == 'BDI') %>% ungroup() %>% select('earliest_max_string_date') == all_data %>% filter(Country_Code == 'BDI') %>% select('earliest_max_string_date')## earliest_max_string_date
## [1,] TRUEearliest_max_string_date %>% filter(Country_Code == 'YEM') %>% ungroup() %>% select('earliest_max_string_date') == all_data %>% filter(Country_Code == 'YEM') %>% select('earliest_max_string_date')## earliest_max_string_date
## [1,] TRUE#11 A) Private health expenditure
#11 A.1 Import the private health file
private_health_2017 <- read_csv("private_health_2017.csv",
skip = 4)## Parsed with column specification:
## cols(
## .default = col_logical(),
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Indicator Name` = col_character(),
## `Indicator Code` = col_character(),
## `2000` = col_double(),
## `2001` = col_double(),
## `2002` = col_double(),
## `2003` = col_double(),
## `2004` = col_double(),
## `2005` = col_double(),
## `2006` = col_double(),
## `2007` = col_double(),
## `2008` = col_double(),
## `2009` = col_double(),
## `2010` = col_double(),
## `2011` = col_double(),
## `2012` = col_double(),
## `2013` = col_double(),
## `2014` = col_double(),
## `2015` = col_double()
## # ... with 2 more columns
## )## See spec(...) for full column specifications.#11 A.2 Change the column name 'Country Code' into 'Country_Code' and also '2017' to 'private_health_2017'
colnames(private_health_2017)[which(names(private_health_2017) == "Country Code")] <- "Country_Code"
colnames(private_health_2017)[which(names(private_health_2017) == '2017')] <- "private_health_2017"
#11 A.3 Use the full_join function to add the private_health_2017 variable into the all_data sheet
all_data <- full_join(all_data, select(private_health_2017, 'Country_Code', 'private_health_2017'), by = 'Country_Code')
#11 A.4 Get rid of country names that are NA since they are not official countries
all_data <- all_data %>% filter(!is.na(Country_Name))
#11 A.5 Add a few test cases
private_health_2017 %>% filter(Country_Code == 'AFG') %>% select('private_health_2017') == all_data %>% filter(Country_Code == 'AFG') %>% select('private_health_2017')## private_health_2017
## [1,] TRUEprivate_health_2017 %>% filter(Country_Code == 'DZA') %>% select('private_health_2017') == all_data %>% filter(Country_Code == 'DZA') %>% select('private_health_2017')## private_health_2017
## [1,] TRUEprivate_health_2017 %>% filter(Country_Code == 'LBR') %>% select('private_health_2017') == all_data %>% filter(Country_Code == 'LBR') %>% select('private_health_2017')## private_health_2017
## [1,] TRUEprivate_health_2017 %>% filter(Country_Code == 'ZWE') %>% select('private_health_2017') == all_data %>% filter(Country_Code == 'ZWE') %>% select('private_health_2017')## private_health_2017
## [1,] TRUEprivate_health_2017 %>% filter(Country_Code == 'CHE') %>% select('private_health_2017') == all_data %>% filter(Country_Code == 'CHE') %>% select('private_health_2017')## private_health_2017
## [1,] TRUEprivate_health_2017 %>% filter(Country_Code == 'LCA') %>% select('private_health_2017') == all_data %>% filter(Country_Code == 'LCA') %>% select('private_health_2017')## private_health_2017
## [1,] TRUE#11 B) Public health expenditure
#11 B.1 Import the file public health expenditure
public_health_2017 <- read_csv("public_health_2017.csv",
skip = 4)## Parsed with column specification:
## cols(
## .default = col_logical(),
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Indicator Name` = col_character(),
## `Indicator Code` = col_character(),
## `2000` = col_double(),
## `2001` = col_double(),
## `2002` = col_double(),
## `2003` = col_double(),
## `2004` = col_double(),
## `2005` = col_double(),
## `2006` = col_double(),
## `2007` = col_double(),
## `2008` = col_double(),
## `2009` = col_double(),
## `2010` = col_double(),
## `2011` = col_double(),
## `2012` = col_double(),
## `2013` = col_double(),
## `2014` = col_double(),
## `2015` = col_double()
## # ... with 2 more columns
## )## See spec(...) for full column specifications.#11 B.2 Change the column name 'Country Code' into 'Country_Code' and also '2017' to 'private_health_2017'
colnames(public_health_2017)[which(names(public_health_2017) == "Country Code")] <- "Country_Code"
colnames(public_health_2017)[which(names(public_health_2017) == '2017')] <- "public_health_2017"
#11 B.3 Use the full_join function to add the private_health_2017 variable into the all_data sheet
all_data <- full_join(all_data, select(public_health_2017, 'Country_Code', 'public_health_2017'), by = 'Country_Code')
#11 B.4 Get rid of country names that are NA since they are not official countries
all_data <- all_data %>% filter(!is.na(Country_Name))
#11 B.5 Add a few test cases
public_health_2017 %>% filter(Country_Code == 'AFG') %>% select('public_health_2017') == all_data %>% filter(Country_Code == 'AFG') %>% select('public_health_2017')## public_health_2017
## [1,] TRUEpublic_health_2017 %>% filter(Country_Code == 'DZA') %>% select('public_health_2017') == all_data %>% filter(Country_Code == 'DZA') %>% select('public_health_2017')## public_health_2017
## [1,] TRUEpublic_health_2017 %>% filter(Country_Code == 'LBR') %>% select('public_health_2017') == all_data %>% filter(Country_Code == 'LBR') %>% select('public_health_2017')## public_health_2017
## [1,] TRUEpublic_health_2017 %>% filter(Country_Code == 'ZWE') %>% select('public_health_2017') == all_data %>% filter(Country_Code == 'ZWE') %>% select('public_health_2017')## public_health_2017
## [1,] TRUEpublic_health_2017 %>% filter(Country_Code == 'CHE') %>% select('public_health_2017') == all_data %>% filter(Country_Code == 'CHE') %>% select('public_health_2017')## public_health_2017
## [1,] TRUEpublic_health_2017 %>% filter(Country_Code == 'LCA') %>% select('public_health_2017') == all_data %>% filter(Country_Code == 'LCA') %>% select('public_health_2017')## public_health_2017
## [1,] TRUE#12 A) GNI per capita
#12 A.1 Import the GNI file
GNI_per_capita_2018 <- read_csv("GNI_per_capita_2018.csv",
skip = 4)## Parsed with column specification:
## cols(
## .default = col_double(),
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Indicator Name` = col_character(),
## `Indicator Code` = col_character(),
## `1960` = col_logical(),
## `1961` = col_logical(),
## `2019` = col_logical()
## )## See spec(...) for full column specifications.#12 A.2 Change the column name 'Country Code' into 'Country_Code' and also '2018' to 'GNI_per_capita_2018'
colnames(GNI_per_capita_2018)[which(names(GNI_per_capita_2018) == "Country Code")] <- "Country_Code"
colnames(GNI_per_capita_2018)[which(names(GNI_per_capita_2018) == '2018')] <- "GNI_per_capita_2018"
#12 A.3 Use the full_join function to add the GNI per capita variable into the all_data sheet
all_data <- full_join(all_data, select(GNI_per_capita_2018, 'Country_Code', 'GNI_per_capita_2018'), by = 'Country_Code')
#12 A.4 Get rid of country names that are NA since they are not official countries
all_data <- all_data %>% filter(!is.na(Country_Name))
#12 A.5 Taiwan 2020 GNI per capita retrieved data from https://eng.stat.gov.tw/point.asp?index=1
all_data$GNI_per_capita_2018[all_data$Country_Code == "TWN"] = 27867
#12 A.6 Add a few test cases
GNI_per_capita_2018 %>% filter(Country_Code == 'AFG') %>% select('GNI_per_capita_2018') == all_data %>% filter(Country_Code == 'AFG') %>% select('GNI_per_capita_2018')## GNI_per_capita_2018
## [1,] TRUEGNI_per_capita_2018 %>% filter(Country_Code == 'ATG') %>% select('GNI_per_capita_2018') == all_data %>% filter(Country_Code == 'ATG') %>% select('GNI_per_capita_2018')## GNI_per_capita_2018
## [1,] TRUEGNI_per_capita_2018 %>% filter(Country_Code == 'AUS') %>% select('GNI_per_capita_2018') == all_data %>% filter(Country_Code == 'AUS') %>% select('GNI_per_capita_2018')## GNI_per_capita_2018
## [1,] TRUEGNI_per_capita_2018 %>% filter(Country_Code == 'ZMB') %>% select('GNI_per_capita_2018') == all_data %>% filter(Country_Code == 'ZMB') %>% select('GNI_per_capita_2018')## GNI_per_capita_2018
## [1,] TRUEGNI_per_capita_2018 %>% filter(Country_Code == 'UKR') %>% select('GNI_per_capita_2018') == all_data %>% filter(Country_Code == 'UKR') %>% select('GNI_per_capita_2018')## GNI_per_capita_2018
## [1,] TRUEGNI_per_capita_2018 %>% filter(Country_Code == 'TUV') %>% select('GNI_per_capita_2018') == all_data %>% filter(Country_Code == 'TUV') %>% select('GNI_per_capita_2018')## GNI_per_capita_2018
## [1,] TRUE#12 B) Out-of-pocket expenditure per capita
#12 B.1 Import the out of pocket expenditure per capita file
outofpocket_per_capita_2017 <- read_csv("outofpocket_per_capita_2017/outofpocket_per_capita_2017.csv",
skip = 4)## Warning: Missing column names filled in: 'X65' [65]## Parsed with column specification:
## cols(
## .default = col_logical(),
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Indicator Name` = col_character(),
## `Indicator Code` = col_character(),
## `2000` = col_double(),
## `2001` = col_double(),
## `2002` = col_double(),
## `2003` = col_double(),
## `2004` = col_double(),
## `2005` = col_double(),
## `2006` = col_double(),
## `2007` = col_double(),
## `2008` = col_double(),
## `2009` = col_double(),
## `2010` = col_double(),
## `2011` = col_double(),
## `2012` = col_double(),
## `2013` = col_double(),
## `2014` = col_double(),
## `2015` = col_double()
## # ... with 2 more columns
## )## See spec(...) for full column specifications.#12 B.2 Change the column name 'Country Code' into 'Country_Code' and also '2018' to 'GNI_per_capita_2018'
colnames(outofpocket_per_capita_2017)[which(names(outofpocket_per_capita_2017) == "Country Code")] <- "Country_Code"
colnames(outofpocket_per_capita_2017)[which(names(outofpocket_per_capita_2017) == '2017')] <- "outofpocket_per_capita_2017"
#12 B.3 Use the full_join function to add the out of pocket expenditure per capita variable into the all_data sheet
all_data <- full_join(all_data, select(outofpocket_per_capita_2017, 'Country_Code', 'outofpocket_per_capita_2017'), by = 'Country_Code')
#12 B.4 Get rid of country names that are NA since they are not official countries
all_data <- all_data %>% filter(!is.na(Country_Name))
#12 B.5 Add a few test cases
outofpocket_per_capita_2017 %>% filter(Country_Code == 'AFG') %>% select('outofpocket_per_capita_2017') == all_data %>% filter(Country_Code == 'AFG') %>% select('outofpocket_per_capita_2017')## outofpocket_per_capita_2017
## [1,] TRUEoutofpocket_per_capita_2017 %>% filter(Country_Code == 'DZA') %>% select('outofpocket_per_capita_2017') == all_data %>% filter(Country_Code == 'DZA') %>% select('outofpocket_per_capita_2017')## outofpocket_per_capita_2017
## [1,] TRUEoutofpocket_per_capita_2017 %>% filter(Country_Code == 'BTN') %>% select('outofpocket_per_capita_2017') == all_data %>% filter(Country_Code == 'BTN') %>% select('outofpocket_per_capita_2017')## outofpocket_per_capita_2017
## [1,] TRUEoutofpocket_per_capita_2017 %>% filter(Country_Code == 'GHA') %>% select('outofpocket_per_capita_2017') == all_data %>% filter(Country_Code == 'GHA') %>% select('outofpocket_per_capita_2017')## outofpocket_per_capita_2017
## [1,] TRUEoutofpocket_per_capita_2017 %>% filter(Country_Code == 'IRL') %>% select('outofpocket_per_capita_2017') == all_data %>% filter(Country_Code == 'IRL') %>% select('outofpocket_per_capita_2017')## outofpocket_per_capita_2017
## [1,] TRUEoutofpocket_per_capita_2017 %>% filter(Country_Code == 'KGZ') %>% select('outofpocket_per_capita_2017') == all_data %>% filter(Country_Code == 'KGZ') %>% select('outofpocket_per_capita_2017')## outofpocket_per_capita_2017
## [1,] TRUE13 Acute care beds OECD
# 13 A.1 Import the acute care beds file from OECD
acute_care_beds <- read_csv("acute_care_beds.csv")## Warning: Missing column names filled in: 'X1' [1]## Parsed with column specification:
## cols(
## X1 = col_character(),
## Variable = col_character(),
## UNIT = col_character(),
## Measure = col_character(),
## COU = col_character(),
## Country = col_character(),
## YEA = col_double(),
## Year = col_double(),
## Value = col_double(),
## `Flag Codes` = col_character(),
## Flags = col_character()
## )# 13 A.2 Change the column name of 'COU' into 'Country_Code'
colnames(acute_care_beds)[which(names(acute_care_beds) == "COU")] <- "Country_Code"
# 13 A.3 Filter and select necessary rows and columns to get latest date information on beds
acute_beds_OECD_2016 <- acute_care_beds %>% group_by(Country_Code) %>% filter(Year == 2016, Variable == 'Curative (acute) care beds', Measure == 'Per 1 000 population') %>% select(Country_Code, Value)
# 13 A.4 Use the full_join function with the all data to add the acute_beds_OECD_2016 beds
all_data <- full_join(all_data, acute_beds_OECD_2016, by = 'Country_Code')
# 13 A.5 Change the column name of 'Value' into 'acute_beds_OECD_2016_per1000'
colnames(all_data)[which(names(all_data) == "Value")] <- "acute_beds_OECD_2016_per1000"
# 13 A.6 Add a few test cases
acute_beds_OECD_2016 %>% filter(Country_Code == 'AUT') %>% ungroup() %>% select(Value) == all_data %>% filter(Country_Code == 'AUT') %>% select('acute_beds_OECD_2016_per1000')## Value
## [1,] TRUEacute_beds_OECD_2016 %>% filter(Country_Code == 'BEL') %>% ungroup() %>% select(Value) == all_data %>% filter(Country_Code == 'BEL') %>% select('acute_beds_OECD_2016_per1000')## Value
## [1,] TRUEacute_beds_OECD_2016 %>% filter(Country_Code == 'GRC') %>% ungroup() %>% select(Value) == all_data %>% filter(Country_Code == 'GRC') %>% select('acute_beds_OECD_2016_per1000')## Value
## [1,] TRUEacute_beds_OECD_2016 %>% filter(Country_Code == 'ISL') %>% ungroup() %>% select(Value) == all_data %>% filter(Country_Code == 'ISL') %>% select('acute_beds_OECD_2016_per1000')## Value
## [1,] TRUEacute_beds_OECD_2016 %>% filter(Country_Code == 'JPN') %>% ungroup() %>% select(Value) == all_data %>% filter(Country_Code == 'JPN') %>% select('acute_beds_OECD_2016_per1000')## Value
## [1,] TRUE14 A) Had Mers
#14 A.1 Import the Mers file
Countries_with_MERS <- read_excel("Countries_with_MERSSARS.xlsx")## New names:
## * Deaths -> Deaths...5
## * Deaths -> Deaths...8#14 A.2 Change the column variable name from 'Country Code' to 'Country_Code'
colnames(Countries_with_MERS)[which(names(Countries_with_MERS) == "Country Code")] <- "Country_Code"
#14 A.3 Use the full join function to add mers cases to all_data
all_data <- full_join(all_data, select(Countries_with_MERS, 'Country_Code', 'Mers_Cases'), by = 'Country_Code')
#14 A.4 Add a few test cases
Countries_with_MERS %>% filter(Country_Code == 'AUT') %>% select(Mers_Cases) == all_data %>% filter(Country_Code == 'AUT') %>% select('Mers_Cases')## Mers_Cases
## [1,] TRUECountries_with_MERS %>% filter(Country_Code == 'JOR') %>% select(Mers_Cases) == all_data %>% filter(Country_Code == 'JOR') %>% select('Mers_Cases')## Mers_Cases
## [1,] TRUECountries_with_MERS %>% filter(Country_Code == 'KOR') %>% select(Mers_Cases) == all_data %>% filter(Country_Code == 'KOR') %>% select('Mers_Cases')## Mers_Cases
## [1,] TRUECountries_with_MERS %>% filter(Country_Code == 'SAU') %>% select(Mers_Cases) == all_data %>% filter(Country_Code == 'SAU') %>% select('Mers_Cases')## Mers_Cases
## [1,] TRUE#14 A.5 Turn all NAs into zero
all_data$Mers_Cases[is.na(all_data$Mers_Cases)] <- 014 B) Had Sars
#14 B.1 Import the Sars file
Countries_with_SARS <- read_excel("Countries_with_MERSSARS.xlsx")## New names:
## * Deaths -> Deaths...5
## * Deaths -> Deaths...8#14 B.2 Change the column variable name from 'Country Code' to 'Country_Code'
colnames(Countries_with_SARS)[which(names(Countries_with_SARS) == "Country Code")] <- "Country_Code"
#14 B.3 Use the full join function to add mers cases to all_data
all_data <- full_join(all_data, select(Countries_with_SARS, 'Country_Code', 'SARS_Cases'), by = 'Country_Code')
#14. B.4 Add a few test cases
Countries_with_SARS %>% filter(Country_Code == 'CHN') %>% select(SARS_Cases) == all_data %>% filter(Country_Code == 'CHN') %>% select('SARS_Cases')## SARS_Cases
## [1,] TRUECountries_with_SARS %>% filter(Country_Code == 'CAN') %>% select(SARS_Cases) == all_data %>% filter(Country_Code == 'CAN') %>% select('SARS_Cases')## SARS_Cases
## [1,] TRUECountries_with_SARS %>% filter(Country_Code == 'TWN') %>% select(SARS_Cases) == all_data %>% filter(Country_Code == 'TWN') %>% select('SARS_Cases')## SARS_Cases
## [1,] TRUECountries_with_SARS %>% filter(Country_Code == 'MAC') %>% select(SARS_Cases) == all_data %>% filter(Country_Code == 'MAC') %>% select('SARS_Cases')## SARS_Cases
## [1,] TRUEall_data$SARS_Cases[is.na(all_data$SARS_Cases)] <- 0#15 A) total number of cases (Needs to be periodically updated from website: https://ourworldindata.org/covid-cases)
# 15 A.1 Import the total number of cases file
#total_cases_covid_19 <- read_csv("total-cases-covid-19-7-13.csv")
total_cases_covid_19 <- read_csv("Covid_cases_update.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## iso_code = col_character(),
## continent = col_character(),
## location = col_character(),
## date = col_date(format = ""),
## new_tests = col_logical(),
## total_tests = col_logical(),
## total_tests_per_thousand = col_logical(),
## new_tests_per_thousand = col_logical(),
## new_tests_smoothed = col_logical(),
## new_tests_smoothed_per_thousand = col_logical(),
## tests_per_case = col_logical(),
## positive_rate = col_logical(),
## tests_units = col_logical()
## )## See spec(...) for full column specifications.## Warning: 104154 parsing failures.
## row col expected actual file
## 1127 new_tests 1/0/T/F/TRUE/FALSE 2.0 'Covid_cases_update.csv'
## 1127 total_tests 1/0/T/F/TRUE/FALSE 2.0 'Covid_cases_update.csv'
## 1127 total_tests_per_thousand 1/0/T/F/TRUE/FALSE 0.0 'Covid_cases_update.csv'
## 1127 new_tests_per_thousand 1/0/T/F/TRUE/FALSE 0.0 'Covid_cases_update.csv'
## 1127 tests_units 1/0/T/F/TRUE/FALSE people tested 'Covid_cases_update.csv'
## .... ........................ .................. ............. ........................
## See problems(...) for more details.#15 A.2 Change the column variable name from 'Code' to 'Country_Code' and the cases name too
colnames(total_cases_covid_19)[which(names(total_cases_covid_19) == "iso_code")] <- "Country_Code"
#15 A.3 Necessary filtering and grouping by Country_Code to get the latest case count. This code only selects the last date of cumulative cases recorded for each country.
total_cases_covid_19 <- total_cases_covid_19 %>% group_by(Country_Code) %>% filter(date == tail(date, n = 1)) %>% ungroup() %>% select(Country_Code, total_cases)
#15 A.4 Change the Kosovo country name from OWID-KOS to KOS
total_cases_covid_19$Country_Code[total_cases_covid_19$Country_Code == 'OWID_KOS'] <- 'XKX'
#15 A.5 Get rid of all NA in Country_Code in total_cases_covid_19
total_cases_covid_19 <- total_cases_covid_19 %>% filter(!is.na(Country_Code))
#15 A.6 Add the total_cases to the variable column of all_data
all_data <- full_join(all_data, total_cases_covid_19, by = 'Country_Code')
#15 A.8 Get rid of all NA in Country_Name in all_data
all_data <- all_data %>% filter(!is.na(Country_Name))
#15 A.9 HKG total cases: https://coronavirus.jhu.edu/map.html
all_data$total_cases[all_data$Country_Code == 'HKG'] = 1106
#15 A.10 Add some cases
total_cases_covid_19 %>% filter(Country_Code == 'IND') %>% select(total_cases) == all_data %>% filter(Country_Code == 'IND') %>% select('total_cases')## total_cases
## [1,] TRUEtotal_cases_covid_19 %>% filter(Country_Code == 'IRQ') %>% select(total_cases) == all_data %>% filter(Country_Code == 'IRQ') %>% select('total_cases')## total_cases
## [1,] TRUEtotal_cases_covid_19 %>% filter(Country_Code == 'NGA') %>% select(total_cases) == all_data %>% filter(Country_Code == 'NGA') %>% select('total_cases')## total_cases
## [1,] TRUEtotal_cases_covid_19 %>% filter(Country_Code == 'SVN') %>% select(total_cases) == all_data %>% filter(Country_Code == 'SVN') %>% select('total_cases')## total_cases
## [1,] TRUEtotal_cases_covid_19 %>% filter(Country_Code == 'USA') %>% select(total_cases) == all_data %>% filter(Country_Code == 'USA') %>% select('total_cases')## total_cases
## [1,] TRUE#15 B) total number of deaths
#15 B.1 Import the total number of deaths file
#total_deaths_covid_19 <- read_csv("total-deaths-covid-19-7-13.csv")
total_deaths_covid_19 <- read_csv("new_covid_deaths.csv")## Parsed with column specification:
## cols(
## .default = col_double(),
## iso_code = col_character(),
## continent = col_character(),
## location = col_character(),
## date = col_date(format = ""),
## new_tests = col_logical(),
## total_tests = col_logical(),
## total_tests_per_thousand = col_logical(),
## new_tests_per_thousand = col_logical(),
## new_tests_smoothed = col_logical(),
## new_tests_smoothed_per_thousand = col_logical(),
## tests_per_case = col_logical(),
## positive_rate = col_logical(),
## tests_units = col_logical()
## )## See spec(...) for full column specifications.## Warning: 104154 parsing failures.
## row col expected actual file
## 1127 new_tests 1/0/T/F/TRUE/FALSE 2.0 'new_covid_deaths.csv'
## 1127 total_tests 1/0/T/F/TRUE/FALSE 2.0 'new_covid_deaths.csv'
## 1127 total_tests_per_thousand 1/0/T/F/TRUE/FALSE 0.0 'new_covid_deaths.csv'
## 1127 new_tests_per_thousand 1/0/T/F/TRUE/FALSE 0.0 'new_covid_deaths.csv'
## 1127 tests_units 1/0/T/F/TRUE/FALSE people tested 'new_covid_deaths.csv'
## .... ........................ .................. ............. ......................
## See problems(...) for more details.#15 B.2 Change the column variable name from 'Code' to 'Country_Code' and the cases name too
colnames(total_deaths_covid_19)[which(names(total_deaths_covid_19) == "iso_code")] <- "Country_Code"
#15 B.3 Necessary filtering and grouping by Country_Code to get the latest death count
total_deaths_covid_19 <- total_deaths_covid_19 %>% group_by(Country_Code) %>% filter(date == tail(date, n = 1)) %>% ungroup() %>% select(Country_Code, total_deaths)
#15 B.4 Change the Kosovo country name from OWID-KOS to KOS
total_deaths_covid_19$Country_Code[total_deaths_covid_19$Country_Code == 'OWID_KOS'] <- 'XKX'
#15 B.5 Get rid of all NA in Country_Code in total_cases_covid_19
total_deaths_covid_19 <- total_deaths_covid_19 %>% filter(!is.na(Country_Code))
#15 B.6 Add the total_deaths to the variable column of all_data
all_data <- full_join(all_data, total_deaths_covid_19, by = 'Country_Code')
#15 B.7 Get rid of all NA in Country_Name in all_data
all_data <- all_data %>% filter(!is.na(Country_Name))
#15 B.8 HKG total total deaths https://coronavirus.jhu.edu/map.html
all_data$total_deaths[all_data$Country_Code == 'HKG'] = 4
#15 B.9 Add some cases
total_deaths_covid_19 %>% filter(Country_Code == 'IND') %>% select(total_deaths) == all_data %>% filter(Country_Code == 'IND') %>% select('total_deaths')## total_deaths
## [1,] TRUEtotal_deaths_covid_19 %>% filter(Country_Code == 'AFG') %>% select(total_deaths) == all_data %>% filter(Country_Code == 'AFG') %>% select('total_deaths')## total_deaths
## [1,] TRUEtotal_deaths_covid_19 %>% filter(Country_Code == 'USA') %>% select(total_deaths) == all_data %>% filter(Country_Code == 'USA') %>% select('total_deaths')## total_deaths
## [1,] TRUEtotal_deaths_covid_19 %>% filter(Country_Code == 'DZA') %>% select(total_deaths) == all_data %>% filter(Country_Code == 'DZA') %>% select('total_deaths')## total_deaths
## [1,] TRUE16 A) Date of First Case
# 16 A.1 Import the file of first date of case
date_first_case <- read_csv("daily-covid-cases-deaths-7-13.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Date = col_character(),
## `Daily confirmed cases (cases)` = col_double(),
## `Daily confirmed deaths (deaths)` = col_double()
## )#16 A.2 Change the column variable name from 'Code' to 'Country_Code' and the cases name too
colnames(date_first_case)[which(names(date_first_case) == "Code")] <- "Country_Code"
colnames(date_first_case)[which(names(date_first_case) == "Daily confirmed cases (cases)")] <- "cases"
#16 A.3 Get the first date of the first case for each country
date_first_case <- date_first_case %>% group_by(Country_Code) %>% filter(cases != 0)%>% filter(Date == head(Date, n = 1)) %>% ungroup() %>% select(Country_Code, Date)
#16 A.4 Change the Kosovo country name from OWID-KOS to KOS
date_first_case$Country_Code[date_first_case$Date == '16-Mar-20' & date_first_case$Country_Code == 'OWID_KOS'] <- 'XKX'
#16 A.5 Get rid of all NAs in Country_Code
date_first_case <- date_first_case %>% filter(!is.na(Country_Code))
#16 A.6 Change the column name of 'Date' to 'first_date_case'
colnames(date_first_case)[which(names(date_first_case) == "Date")] <- "date_first_case"
#16 A.7 Do a full_join function and add variable to all_data
all_data <- full_join(all_data, date_first_case, by = 'Country_Code')
#16 A.8 Get rid of all NA in Country_Name in all_data
all_data <- all_data %>% filter(!is.na(Country_Name))
#16 A.9 HKG date of first case: https://www.scmp.com/news/hong-kong/health-environment/article/3047193/china-coronavirus-first-case-confirmed-hong-kong
all_data$date_first_case[all_data$Country_Code == 'HKG'] = '23-Jan-20'
#16 A.10 Add a few test cases
date_first_case %>% filter(Country_Code == 'USA') %>% select(date_first_case) == all_data %>% filter(Country_Code == 'USA') %>% select('date_first_case')## date_first_case
## [1,] TRUEdate_first_case %>% filter(Country_Code == 'AFG') %>% select(date_first_case) == all_data %>% filter(Country_Code == 'AFG') %>% select('date_first_case')## date_first_case
## [1,] TRUEdate_first_case %>% filter(Country_Code == 'KOR') %>% select(date_first_case) == all_data %>% filter(Country_Code == 'KOR') %>% select('date_first_case')## date_first_case
## [1,] TRUEdate_first_case %>% filter(Country_Code == 'AGO') %>% select(date_first_case) == all_data %>% filter(Country_Code == 'AGO') %>% select('date_first_case')## date_first_case
## [1,] TRUEdate_first_case %>% filter(Country_Code == 'AUS') %>% select(date_first_case) == all_data %>% filter(Country_Code == 'AUS') %>% select('date_first_case')## date_first_case
## [1,] TRUE16 B) Date of First Death
#16 B.1 Import the file of first date of death
date_first_death <- read_csv("daily-covid-cases-deaths-7-13.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Date = col_character(),
## `Daily confirmed cases (cases)` = col_double(),
## `Daily confirmed deaths (deaths)` = col_double()
## )#16 B.2 Change the column variable name from 'Code' to 'Country_Code' and the deaths name too
colnames(date_first_death)[which(names(date_first_death) == "Code")] <- "Country_Code"
colnames(date_first_death)[which(names(date_first_death) == "Daily confirmed deaths (deaths)")] <- "date_first_death"
#16 B.3 Get the first date of the first date of death for each country
date_first_death <- date_first_death %>% filter(date_first_death != 0) %>% group_by(Country_Code) %>% filter(Date == head(Date, n = 1)) %>% ungroup() %>% select(Country_Code, Date)
#16 B.4 Change the Kosovo country name from OWID-KOS to KOS
date_first_death$Country_Code[date_first_death$Date == '23-Mar-20' & date_first_death$Country_Code == 'OWID_KOS'] <- 'XKX'
#16 B.5 Get rid of all NAs in Country_Code
date_first_death <- date_first_death %>% filter(!is.na(Country_Code))
#16 B.6 Change the column name of 'Date' to 'first_date_case'
colnames(date_first_death)[which(names(date_first_death) == "Date")] <- "date_first_death"
#16 B.7 Do a full_join function and add variable to all_data
all_data <- full_join(all_data, date_first_death, by = 'Country_Code')
#16 B.8 Get rid of all NA in Country_Name in all_data
all_data <- all_data %>% filter(!is.na(Country_Name))
#16 B.9 HKG date of first death:https://www.straitstimes.com/asia/east-asia/hong-kong-reports-first-death-from-coronavirus
all_data$date_first_death[all_data$Country_Code == 'HKG'] = '4-Feb-20'
#16 B.10 Add a few test cases
date_first_death %>% filter(Country_Code == 'AFG') %>% select(date_first_death) == all_data %>% filter(Country_Code == 'AFG') %>% select('date_first_death')## date_first_death
## [1,] TRUEdate_first_death %>% filter(Country_Code == 'USA') %>% select(date_first_death) == all_data %>% filter(Country_Code == 'USA') %>% select('date_first_death')## date_first_death
## [1,] TRUEdate_first_death %>% filter(Country_Code == 'MEX') %>% select(date_first_death) == all_data %>% filter(Country_Code == 'MEX') %>% select('date_first_death')## date_first_death
## [1,] TRUEdate_first_death %>% filter(Country_Code == 'KOR') %>% select(date_first_death) == all_data %>% filter(Country_Code == 'KOR') %>% select('date_first_death')## date_first_death
## [1,] TRUEdate_first_death %>% filter(Country_Code == 'ZWE') %>% select(date_first_death) == all_data %>% filter(Country_Code == 'ZWE') %>% select('date_first_death')## date_first_death
## [1,] TRUE#17 Date when daily case/death was maximum (3- airtable) and also max daily case per 10,000 inhabitants (Will need to be regularly updated from website: https://ourworldindata.org/grapher/daily-covid-cases-deaths)
#17.1 Import the daily cases file
max_daily_case <- read_csv("daily-covid-cases-deaths-8-6.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Date = col_character(),
## `Daily confirmed cases (cases)` = col_double(),
## `Daily confirmed deaths (deaths)` = col_double()
## )#17.2 Change the column names
colnames(max_daily_case)[which(names(max_daily_case) == "Code")] <- "Country_Code"
colnames(max_daily_case)[which(names(max_daily_case) == "Daily confirmed cases (cases)")] <- "daily_cases"
colnames(max_daily_case)[which(names(max_daily_case) == "Daily confirmed deaths (deaths)")] <- "daily_deaths"
#17.3 Find out the date of the max daily case and max daily death for each country
max_daily_death <- max_daily_case %>% group_by(Country_Code) %>% filter(daily_deaths == max(daily_deaths, na.rm = TRUE)) %>% group_by(Country_Code) %>% filter(Date == head(Date, n = 1))
max_daily_case <- max_daily_case %>% group_by(Country_Code) %>% filter(daily_cases == max(daily_cases, na.rm = TRUE)) %>% group_by(Country_Code) %>% filter(Date == head(Date, n=1))
#17.4 Change the Kosovo country name from OWID-KOS to KOS
max_daily_case$Country_Code[max_daily_case$Date == 'Apr 21, 2020' & max_daily_case$Country_Code == 'OWID_KOS'] <- 'XKX'
max_daily_death$Country_Code[max_daily_death$Date == 'May 5, 2020' & max_daily_death$Country_Code == 'OWID_KOS'] <- 'XKX'
#17.5 Get rid of all NAs in Country_Code
max_daily_case <- max_daily_case %>% filter(!is.na(Country_Code))
max_daily_death <- max_daily_death %>% filter(!is.na(Country_Code))
#17.6 Change the column name of 'Date' to 'date_max_daily_case'
colnames(max_daily_case)[which(names(max_daily_case) == "Date")] <- "date_max_daily_case"
colnames(max_daily_death)[which(names(max_daily_death) == "Date")] <- "date_max_daily_death"
#17.7 Do a full_join function and add variable to all_data
all_data <- full_join(all_data, select(max_daily_case, Country_Code, date_max_daily_case), by = 'Country_Code')
all_data <- full_join(all_data, select(max_daily_death, Country_Code, date_max_daily_death), by = 'Country_Code')
all_data <- full_join(all_data, select(max_daily_case, Country_Code, daily_cases), by = 'Country_Code')
colnames(all_data)[which(names(all_data) == "daily_cases")] <- "max_daily_case"
all_data <- full_join(all_data, select(max_daily_death, Country_Code, daily_deaths), by = 'Country_Code')
colnames(all_data)[which(names(all_data) == "daily_deaths")] <- "max_daily_deaths"
all_data$max_daily_case <- all_data$max_daily_case/1000
all_data$max_daily_deaths <- all_data$max_daily_deaths/1000
colnames(all_data)[which(names(all_data) == "max_daily_case")] <- "max_daily_caseper1000inhab"
colnames(all_data)[which(names(all_data) == "max_daily_deaths")] <- "max_daily_deathper1000inhab"
#17.8 Get rid of all NA in Country_Name in all_data
all_data <- all_data %>% filter(!is.na(Country_Name))
#17.9 HKG date max daily case: https://www.statista.com/statistics/1105425/hong-kong-novel-coronavirus-covid19-confirmed-death-recovered-trend/
all_data$date_max_daily_case[all_data$Country_Code == 'HKG'] = 'March 27, 2020'
#17.9 Add a few test cases
max_daily_case %>% filter(Country_Code == 'AFG') %>% ungroup() %>% select(date_max_daily_case) == all_data %>% filter(Country_Code == 'AFG') %>% select('date_max_daily_case')## date_max_daily_case
## [1,] TRUEmax_daily_case %>% filter(Country_Code == 'AGO') %>% ungroup() %>% select(date_max_daily_case) == all_data %>% filter(Country_Code == 'AGO') %>% select('date_max_daily_case')## date_max_daily_case
## [1,] TRUEmax_daily_case %>% filter(Country_Code == 'ATG') %>% ungroup() %>% select(date_max_daily_case) == all_data %>% filter(Country_Code == 'ATG') %>% select('date_max_daily_case')## date_max_daily_case
## [1,] TRUE#18 Calculating Variables: Cases per 1,000 inhabitants, deaths per 100000 inhabitants, deaths per case, tests per 100000 inhabitants, max stringency date - first case date, Mers + Sars > 10
#18.1 Cases per 100000 inhabitants
all_data <- all_data %>% mutate(cases_per_100000inhabitants = (total_cases * 100000)/(pop_2020))
#18.2 Deaths per 100000 inhabitants
all_data <- all_data %>% mutate(deaths_per_100000inhabitants = (total_deaths * 100000)/(pop_2020))
#18.3 Deaths per Case (Case Fatality Ratio)
all_data <- all_data %>% mutate(case_fatality_ratio = total_deaths/total_cases)
#18.4 Tests per 100,000 inhabitants
all_data <- all_data %>% mutate(tests_per_100000inhabitants = (sum_tests * 100000)/(pop_2020))
#18.5 Mers and Sars Cases (Qualitative Data)
all_data <- all_data %>% mutate(mers_sars_cases_over10 = case_when(Mers_Cases + SARS_Cases > 10 ~ 'Yes', TRUE ~ 'No'))
#18.6 The days from the first case to the date of max stringency index
all_data <- all_data %>% mutate(days_from_firstcase_maxstringidx = mdy(all_data$earliest_max_string_date) - mdy(all_data$date_first_case))## Warning: 1 failed to parse.#18.7 Days from first case to date of max daily case
all_data <- all_data %>% mutate(days_from_firstcase_maxdailycase = mdy(all_data$date_max_daily_case) - mdy(all_data$date_first_case))## Warning: 1 failed to parse.#18.8 Days from first case to date of max daily death
all_data <- all_data %>% mutate(days_from_firstcase_maxdailydeath = mdy(all_data$date_max_daily_death) - mdy(all_data$date_first_case))## Warning: 1 failed to parse.#18.9 Speed of max stringency index over time from first case to maximum stringency index
all_data$days_from_firstcase_maxstringidx <- as.numeric(all_data$days_from_firstcase_maxstringidx, units = "days")
all_data <- all_data %>% mutate(max_string_index_over_time_to_max = max_stringency_index/days_from_firstcase_maxstringidx)
#18.10 Speed of max cases per inhab over time from first case to date of max daily cases
all_data$days_from_firstcase_maxdailycase <- as.numeric(all_data$days_from_firstcase_maxdailycase, units = "days")
all_data <- all_data %>% mutate(max_daily_case_perinhab_over_time_to_maxcase = max_daily_caseper1000inhab/days_from_firstcase_maxdailycase)
#18.11 Speed of max deaths per inhab over time from first case to date of max daily deaths
all_data$days_from_firstcase_maxdailydeath <- as.numeric(all_data$days_from_firstcase_maxdailydeath, units = "days")
all_data <- all_data %>% mutate(max_daily_deaths_perinhab_over_time_tomaxdeath = max_daily_deathper1000inhab/days_from_firstcase_maxdailydeath)
#18.9 date of max daily cases minus date of max string index
all_data <- all_data %>% mutate(date_max_daily_case_minus_max_string_index = mdy(all_data$date_max_daily_case) - mdy(all_data$earliest_max_string_date))#19 Total Number of Hospital Beds by Country
#19.1 Import the total number of hospital beds file
hospital_beds_per_100000_people <- read_csv("hospital-beds-per-1000-people.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Hospital beds (per 100,000)` = col_double()
## )#19.2 Change the name for column names
colnames(hospital_beds_per_100000_people)[which(names(hospital_beds_per_100000_people) == "Code")] <- "Country_Code"
colnames(hospital_beds_per_100000_people)[which(names(hospital_beds_per_100000_people) == "Hospital beds (per 100,000)")] <- "hospital_beds_per_100000"
#19.3 Filter the hospital beds datasheet to only select the most recent data, which is from 2015.
hospital_beds_per_100000_people <- hospital_beds_per_100000_people %>% group_by(Country_Code) %>% filter(Year == tail(Year, n = 1)) %>% ungroup() %>% filter(Year >= 2015) %>% select(Country_Code, hospital_beds_per_100000)
#19.4 Do the full_join and add to all_data
all_data <- full_join(all_data, hospital_beds_per_100000_people, by = 'Country_Code')#20 Obesity https://ourworldindata.org/obesity
#20.1 Import the obesity file
obese <- read_csv("share-of-adults-defined-as-obese.csv")## Parsed with column specification:
## cols(
## Entity = col_character(),
## Code = col_character(),
## Year = col_double(),
## `Share of adults who are obese (%)` = col_double()
## )#20.2 Change the name for column names
colnames(obese)[which(names(obese) == "Share of adults who are obese (%)")] <- "obesity_prevalnece"
colnames(obese)[which(names(obese) == "Code")] <- "Country_Code"
#20.3 Filter the obese datasheet
obese <- obese %>% group_by(Country_Code) %>% filter(Year == tail(Year, n = 1)) %>% ungroup() %>% select(Country_Code, obesity_prevalnece)
#20.4 remove all the NA country codes
obese <- obese %>% filter(!is.na(Country_Code))
#20.5 Do a full join with the all_data
all_data <- full_join(all_data, obese, by = 'Country_Code')#21 Diabetes prevalence
https://data.worldbank.org/indicator/SH.STA.DIAB.ZS
#21.1 Import the diabetes prevalence file
diabetes_prevalence <- read_csv("diabetes_prevalence/diabetes_prevalence.csv",
skip = 3)## Warning: Missing column names filled in: 'X65' [65]## Parsed with column specification:
## cols(
## .default = col_logical(),
## `Country Name` = col_character(),
## `Country Code` = col_character(),
## `Indicator Name` = col_character(),
## `Indicator Code` = col_character(),
## `2010` = col_double(),
## `2019` = col_double()
## )## See spec(...) for full column specifications.#21.2 Change a few column names
colnames(diabetes_prevalence)[which(names(diabetes_prevalence) == "Country Code")] <- "Country_Code"
colnames(diabetes_prevalence)[which(names(diabetes_prevalence) == "2019")] <- "diabetes_prevalence"
#21.3 Select only necessary columns and then do a full join
diabetes_prevalence <- diabetes_prevalence %>% select(Country_Code, diabetes_prevalence)
all_data <- full_join(all_data, diabetes_prevalence, by = "Country_Code")
#21.5 Get rid of nas for the country name
all_data <- all_data %>% filter(!is.na(Country_Name))#22 High, Low compared to median
#22.1 Create two new columns of the medians of cases per inhab and deaths per inhab
median_cases <- median(all_data$cases_per_100000inhabitants, na.rm = TRUE)
median_deaths <- median(all_data$deaths_per_100000inhabitants, na.rm = TRUE)
#22.2 Make two new variable columns with cases and deaths as high, low or high, high etc.
all_data <- all_data %>% mutate(case_high_low = case_when(cases_per_100000inhabitants > median_cases ~ 'Case High', cases_per_100000inhabitants < median_cases ~ 'Case Low'))
all_data <- all_data %>% mutate(death_high_low = case_when(deaths_per_100000inhabitants > median_deaths ~ 'Death High', deaths_per_100000inhabitants < median_deaths ~ 'Death Low'))
#22.3 Create new column that indicated whether cases and deaths is high high to low low
all_data <- all_data %>% mutate(case_death_high_low = case_when(cases_per_100000inhabitants > median_cases & deaths_per_100000inhabitants > median_deaths ~ 'Case_High Death_High', cases_per_100000inhabitants < median_cases & deaths_per_100000inhabitants > median_deaths ~ 'Case_Low Death_High',cases_per_100000inhabitants > median_cases & deaths_per_100000inhabitants < median_deaths ~ 'Case_High Death_Low', cases_per_100000inhabitants < median_cases & deaths_per_100000inhabitants < median_deaths ~ 'Case_Low Death_Low'))#23 Preparing for the PCA graphs
#23.1 Make a new variable column that takes sum of Sars and Mers cases for each country. This will be one of the variables used in the PCA.
all_data <- all_data %>% mutate(total_SARS_MERS = Mers_Cases + SARS_Cases)
#23.2 Make a copy of all_data into PCA_alldata
PCA_alldata <- all_data
#23.3 Select only the variables that are in the active_variables vector. Below are four colums for four different PCA's. 1) All Active Variables With Acute Bed (Less Countries b/c of more NA's due to the acute beds variable), 2) Less Active Variables With Acute Bed (Less Countries b/c of more NA's due to the acute beds variable), 3) All Active Variables With No Acute Beds (More Countries b/c of less NA's), 4) Less Active Variables With No Acute Bed (More Countries b/c of less NA's) #note: Less Active Variables: No GDP_2018, female_prop_2018, max_stringency_index, total_SARS_MERS, smoking_prevalence_2016
#1) All Variables With (Acute Beds)
#active_variables_plus_casedeath <- c("Country_Name", "GDP_2018", "MedianAge_2020", "smoking_prevalence_2016", "freeidx_2018", "female_prop_2018", "max_stringency_index", "public_health_2017", "GNI_per_capita_2018", "outofpocket_per_capita_2017", "acute_beds_OECD_2016_per1000", "tests_per_100000inhabitants", "total_SARS_MERS", "date_max_daily_case_minus_max_string_index", "hospital_beds_per_100000", "obesity_prevalnece", "diabetes_prevalence", "cases_per_100000inhabitants", "deaths_per_100000inhabitants")
#2) Less Active Variables With (Acute Beds)
#active_variables_plus_casedeath <- c("Country_Name", "MedianAge_2020", "freeidx_2018", "public_health_2017", "GNI_per_capita_2018", "outofpocket_per_capita_2017", "acute_beds_OECD_2016_per1000", "tests_per_100000inhabitants", "date_max_daily_case_minus_max_string_index", "hospital_beds_per_100000", "obesity_prevalnece", "diabetes_prevalence", "cases_per_100000inhabitants", "deaths_per_100000inhabitants")
#3) All variables without (Acute Beds)
#active_variables_plus_casedeath <- c("Country_Name", "GDP_2018", "MedianAge_2020", "smoking_prevalence_2016", "freeidx_2018", "female_prop_2018", "max_stringency_index", "public_health_2017", "GNI_per_capita_2018", "outofpocket_per_capita_2017", "tests_per_100000inhabitants", "total_SARS_MERS", "date_max_daily_case_minus_max_string_index", "hospital_beds_per_100000", "obesity_prevalnece", "diabetes_prevalence", "cases_per_100000inhabitants", "deaths_per_100000inhabitants")
# 4) Less Active Variables (Without Acute Beds)
active_variables_plus_casedeath <- c("Country_Name", "MedianAge_2020", "freeidx_2018", "public_health_2017", "GNI_per_capita_2018", "outofpocket_per_capita_2017", "tests_per_100000inhabitants", "date_max_daily_case_minus_max_string_index", "hospital_beds_per_100000", "obesity_prevalnece", "diabetes_prevalence", "cases_per_100000inhabitants", "deaths_per_100000inhabitants")
PCA_alldata <- PCA_alldata %>% select(all_of(active_variables_plus_casedeath))
#23.4 Filter out the countries that have NA values
PCA_alldata <- na.omit(PCA_alldata)
#23.4 Calculate the medians of cases per inhab and deaths per inhab for PCA_alldata
PCA_median_cases <- median(PCA_alldata$cases_per_100000inhabitants, na.rm = TRUE)
PCA_median_deaths <- median(PCA_alldata$deaths_per_100000inhabitants, na.rm = TRUE)
#23.5 Make two new variable columns with cases and deaths as high, low or high, high etc. These variable columns will later be used to categorize groups during the PCA Analysis.
PCA_alldata <- PCA_alldata %>% mutate(case_high_low = case_when(cases_per_100000inhabitants > PCA_median_cases ~ 'Case High', cases_per_100000inhabitants < PCA_median_cases ~ 'Case Low'))
PCA_alldata <- PCA_alldata %>% mutate(death_high_low = case_when(deaths_per_100000inhabitants > PCA_median_deaths ~ 'Death High', deaths_per_100000inhabitants < PCA_median_deaths ~ 'Death Low'))
#23.6 Create new column that indicated whether cases and deaths is high high to low low. This variable column will later be used to categorize groups during the PCA Analysis.
PCA_alldata <- PCA_alldata %>% mutate(case_death_high_low = case_when(cases_per_100000inhabitants > PCA_median_cases & deaths_per_100000inhabitants > PCA_median_deaths ~ 'Case_High Death_High', cases_per_100000inhabitants < PCA_median_cases & deaths_per_100000inhabitants > PCA_median_deaths ~ 'Case_Low Death_High',cases_per_100000inhabitants > PCA_median_cases & deaths_per_100000inhabitants < PCA_median_deaths ~ 'Case_High Death_Low', cases_per_100000inhabitants < PCA_median_cases & deaths_per_100000inhabitants < PCA_median_deaths ~ 'Case_Low Death_Low'))
#23.7 Make the country name a row name
PCA_alldata <- column_to_rownames(PCA_alldata, "Country_Name")
#23.8 Split into two dataframes of PCA_alldata_active and PCA_supp
#1) All Variables With (Acute Beds)
#PCA_alldata_active <- PCA_alldata %>% select("GDP_2018", "MedianAge_2020", "smoking_prevalence_2016", "freeidx_2018", "female_prop_2018", "max_stringency_index", "public_health_2017", "GNI_per_capita_2018", "outofpocket_per_capita_2017", "acute_beds_OECD_2016_per1000", "tests_per_100000inhabitants", "total_SARS_MERS", "date_max_daily_case_minus_max_string_index", "hospital_beds_per_100000", "obesity_prevalnece", "diabetes_prevalence")
#2) Less Active Variables With (Acute Beds)
#PCA_alldata_active <- PCA_alldata %>% select("MedianAge_2020", "freeidx_2018", "public_health_2017", "GNI_per_capita_2018", "outofpocket_per_capita_2017", "acute_beds_OECD_2016_per1000", "tests_per_100000inhabitants", "date_max_daily_case_minus_max_string_index", "hospital_beds_per_100000", "obesity_prevalnece", "diabetes_prevalence")
#3) All variables without (Acute Beds)
#PCA_alldata_active <- PCA_alldata %>% select("GDP_2018", "MedianAge_2020", "smoking_prevalence_2016", "freeidx_2018", "female_prop_2018", "max_stringency_index", "public_health_2017", "GNI_per_capita_2018", "outofpocket_per_capita_2017", "tests_per_100000inhabitants", "total_SARS_MERS", "date_max_daily_case_minus_max_string_index", "hospital_beds_per_100000", "obesity_prevalnece", "diabetes_prevalence")
#4) Less Active Variables (Without Acute Beds)
PCA_alldata_active <- PCA_alldata %>% select("MedianAge_2020", "freeidx_2018", "public_health_2017", "GNI_per_capita_2018", "outofpocket_per_capita_2017", "tests_per_100000inhabitants", "date_max_daily_case_minus_max_string_index", "hospital_beds_per_100000", "obesity_prevalnece", "diabetes_prevalence")
#for fun practice
#PCA_alldata_active <- PCA_alldata %>% select("MedianAge_2020", "public_health_2017", "GNI_per_capita_2018", "outofpocket_per_capita_2017", "tests_per_100000inhabitants", "hospital_beds_per_100000", "obesity_prevalnece")
PCA_supp <- PCA_alldata %>% select("case_high_low", "death_high_low", "case_death_high_low")#24 PCA Graphs- Scree Plot, Contribution of PC1 and PC2, variable contributions, individual contributions, three graphs based on groups high, low
#24.1 Turn calendar date unit into numeric units. Otherwise, R doesn't perform the PCA analysis since it strictly requires numerical units.
PCA_alldata_active$date_max_daily_case_minus_max_string_index <- as.numeric(PCA_alldata_active$date_max_daily_case_minus_max_string_index, units = "days")
#24.2 Perform the PCA
res.pca <- PCA(PCA_alldata_active, graph = TRUE)
#24.2 Produce the screeplot
PCA_screeplot <- fviz_screeplot(res.pca, addlabels = TRUE, ylim = c(0, 45))
PCA_screeplot
#The 1-4 numerical labels should correspond to the same label as the 4 PCA types when saving the plots as a pdf
#1) All variables with acute bed
#ggsave("PCA_screeplot_allvar_withAcuteBed.pdf", plot = PCA_screeplot, scale = 4, limitsize = FALSE)
#2) Less Variables with acute bed
#ggsave("PCA_screeplot_lessvar_withAcuteBed.pdf", plot = PCA_screeplot, scale = 2, limitsize = FALSE)
#3) All Variables Without Acute Bed
#ggsave("PCA_screeplot_allvar_withoutAcuteBed.pdf", plot = PCA_screeplot, scale = 3, limitsize = FALSE)
#4) Less Variables Without Acute Bed
#ggsave("PCA_screeplot_lessvar_withoutAcuteBed.pdf", plot = PCA_screeplot, scale = 3, limitsize = FALSE)
#24.3 Contributions of variables to PC1 and to PC2
PC1contrib <- fviz_contrib(res.pca, choice = "var", axes = 1, top = 10)
PC1contrib
#1) All variables with acute bed
#ggsave("PC1contrib_allvar_withAcuteBed.pdf", plot = PC1contrib, scale = 4, limitsize = FALSE)
#2) Less Varibles with acute bed
#ggsave("PC1contrib_lessvar_withAcuteBed.pdf", plot = PC1contrib, scale = 2, limitsize = FALSE)
#3) All Variables Without Acute Bed
#ggsave("PC1contrib_allvar_withoutAcuteBed.pdf", plot = PC1contrib, scale = 4, limitsize = FALSE)
#4) Less Variables Without Acute Bed
#ggsave("PC1contrib_lessvar_withoutAcuteBed.pdf", plot = PC1contrib, scale = 3, limitsize = FALSE)
PC2contrib <- fviz_contrib(res.pca, choice = "var", axes = 2, top = 10)
PC2contrib
#1) All Variables With Acute Bed
#ggsave("PC2contrib_allvar_withAcuteBed.pdf", plot = PC2contrib, scale = 4, limitsize = FALSE)
#2) Less Variables With Acute Bed
#ggsave("PC2contrib_lessvar_withAcuteBed.pdf", plot = PC2contrib, scale = 2, limitsize = FALSE)
#3) All Variables Without Acute Bed
#ggsave("PC2contrib_allvar_withoutAcuteBed.pdf", plot = PC2contrib, scale = 4, limitsize = FALSE)
#4) Less Variables Without Acute Bed
#ggsave("PC2contrib_lessvar_withoutAcuteBed.pdf", plot = PC2contrib, scale = 4, limitsize = FALSE)
#24.4 Variable Contributions for dimensions 1 and 2
PCA_var_contribution <- fviz_pca_var(res.pca, col.var = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE, # Avoid text overlapping
ggtheme = theme_minimal())
PCA_var_contribution
#1) All Variables With Acute Bed
#ggsave("PCA_var_contribution_allvar_withAcuteBed.pdf", plot = PCA_var_contribution, scale = 4, limitsize = FALSE)
#2) Less Variables With Acute Bed
#ggsave("PCA_var_contribution_lessvar_withAcuteBed.pdf", plot = PCA_var_contribution, scale = 2, limitsize = FALSE)
#3) All Variables Without Acute Bed
#ggsave("PCA_var_contribution_allvar_withoutAcuteBed.pdf", plot = PCA_var_contribution, scale = 3, limitsize = FALSE)
#4) Less Variables Without Acute Bed
#ggsave("PCA_var_contribution_lessvar_withoutAcuteBed.pdf", plot = PCA_var_contribution, scale = 3, limitsize = FALSE)
#24.5 Individual Plot colored by
PCA_indivi_plot <- fviz_pca_ind(res.pca, col.ind = "contrib",
gradient.cols = c("#00AFBB", "#E7B800", "#FC4E07"),
repel = TRUE # Avoid text overlapping (slow if many points)
)
PCA_indivi_plot
#1) All Variables With Acute Bed
#ggsave("PCA_indivi_plot_allvar_withAcuteBed.pdf", plot = PCA_indivi_plot, scale = 4, limitsize = FALSE)
#2) Less Variables With Acute Bed
#ggsave("PCA_indivi_plot_lessvar_withAcuteBed.pdf", plot = PCA_indivi_plot, scale = 4, limitsize = FALSE)
#3) All Variables Without Acute Bed
#ggsave("PCA_indivi_plot_allvar_withoutAcuteBed.pdf", plot = PCA_indivi_plot, scale = 3, limitsize = FALSE)
#4) Less Variables Without Acute Bed
#ggsave("PCA_indivi_plot_lessvar_withoutAcuteBed.pdf", plot = PCA_indivi_plot, scale = 3, limitsize = FALSE)
#24.6 Three graphs that will group individuals by case high_low and death high_low and both
#a) Group individuals by case_high_low column variable
plot_case_high_low <- fviz_pca_ind(res.pca,
geom.ind = "point", # show points only (nbut not "text")
col.ind = PCA_supp$case_high_low, # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE,# Concentration ellipses
legend.title = "Groups"
)
plot_case_high_low## Too few points to calculate an ellipse## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
#1) All Variables With Acute Bed
#ggsave("plot_case_high_low_allvar_withAcuteBed.pdf", plot = plot_case_high_low, scale = 4, limitsize = FALSE)
#2) Less Variables With Acute Bed
#ggsave("plot_case_high_low_lessvar_withAcuteBed.pdf", plot = plot_case_high_low, scale = 4, limitsize = FALSE)
#3) All Variables Without Acute Bed
#ggsave("plot_case_high_low_allvar_withoutAcuteBed.pdf", plot = plot_case_high_low, scale = 3, limitsize = FALSE)
#4) Less Variables Without Acute Bed
#ggsave("plot_case_high_low_lessvar_withoutAcuteBed.pdf", plot = plot_case_high_low, scale = 3, limitsize = FALSE)
#b) Group individuals by death_high_low column variable
plot_death_high_low <- fviz_pca_ind(res.pca,
geom.ind = "point", # show points only (nbut not "text")
col.ind = PCA_supp$death_high_low, # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07"),
addEllipses = TRUE, # Concentration ellipses
legend.title = "Groups"
)
plot_death_high_low## Too few points to calculate an ellipse## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
#1) All Variables With Acute Bed
#ggsave("plot_death_high_low_allvar_withAcuteBed.pdf", plot = plot_death_high_low, scale = 4, limitsize = FALSE)
#2) Less Variables With Acute Bed
#ggsave("plot_death_high_low_lessvar_withAcuteBed.pdf", plot = plot_death_high_low, scale = 4, limitsize = FALSE)
#3) All Variables Without Acute Bed
#ggsave("plot_death_high_low_allvar_withoutAcuteBed.pdf", plot = plot_death_high_low, scale = 3, limitsize = FALSE)
#4) Less Variables Without Acute Bed
#ggsave("plot_death_high_low_lessvar_withoutAcuteBed.pdf", plot = plot_death_high_low, scale = 3, limitsize = FALSE)
#c) Group individuals by case_death_high_low column variable
plot_case_death_high_low <- fviz_pca_ind(res.pca,
geom.ind = "point", # show points only (nbut not "text")
col.ind = PCA_supp$case_death_high_low, # color by groups
palette = c("#00AFBB", "#E7B800", "#FC4E07", "#FF9900"),
addEllipses = TRUE, # Concentration ellipses
legend.title = "Groups"
)
plot_case_death_high_low## Too few points to calculate an ellipse
## Too few points to calculate an ellipse
## Too few points to calculate an ellipse## Warning: Removed 2 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_point).
#1) All Variables With Acute Bed
#ggsave("plot_case_death_high_low_allvar_withAcuteBed.pdf", plot = plot_case_death_high_low, scale = 4, limitsize = FALSE)
#2) Less Variables With Acute Bed
#ggsave("plot_case_death_high_low_lessvar_withAcuteBed.pdf", plot = plot_case_death_high_low, scale = 4, limitsize = FALSE)
#3) All Variables Without Acute Bed
#ggsave("plot_case_death_high_low_allvar_withoutAcuteBed.pdf", plot = plot_case_death_high_low, scale = 3, limitsize = FALSE)
#4) Less Variables Without Acute Bed
#ggsave("plot_case_death_high_low_lessvar_withoutAcuteBed.pdf", plot = plot_case_death_high_low, scale = 3, limitsize = FALSE)- Creating Standard Graphs
#1 Filter Databases Base on Vectors from Europe and World
#1.1 Make a subset of all_data using a vector with European Countries
Euro <- c("Andorra", "Albania", "Austria", "Belarus", "Belgium", "Bosnia and Herzegovina", "Bulgaria",
"Croatia", "Czechia", "Denmark", "Estonia", "Finland", "France", "Georgia", "Germany",
"Greece", "Greenland", "Hungary", "Iceland", "Ireland", "Italy", "Latvia",
"Liechtenstein", "Lithuania", "Malta", "Montenegro", "Netherlands", "Norway", "Poland",
"Portugal", "Republic of Moldova", "Romania", "Russian Federation", "Serbia", "Slovakia",
"Slovenia", "Spain", "Sweden", "Switzerland", "The former Yugoslav Republic of Macedonia",
"Ukraine", "United Kingdom of Great Britain and Northern Ireland", "Kosovo")
European_Countries <- all_data %>% filter(Country_Name %in% Euro)
#1.2 Make a subset of all_data using a vector with the rest of the other countries
World <- c("Andorra", "Albania", "Austria", "Belarus", "Belgium", "Bosnia and Herzegovina", "Bulgaria",
"Croatia", "Czechia", "Denmark", "Estonia", "Finland", "France", "Georgia", "Germany",
"Greece", "Greenland", "Hungary", "Iceland", "Ireland", "Italy", "Latvia",
"Liechtenstein", "Lithuania", "Malta", "Montenegro", "Netherlands", "Norway", "Poland",
"Portugal", "Republic of Moldova", "Romania", "Russian Federation", "Serbia", "Slovakia",
"Slovenia", "Spain", "Sweden", "Switzerland", "The former Yugoslav Republic of Macedonia",
"Ukraine", "United Kingdom of Great Britain and Northern Ireland", "Kosovo", "Afghanistan", "Albania",
"Argentina", "Armenia", "Australia", "Azerbaijan", "Bahrain", "Bangladesh", "Bhutan", "Bolivia (Plurinational State of)",
"Brazil", "Cambodia", "Canada", "Chile", "China", "China, Hong Kong Special Administrative Region",
"China, Macao Special Administrative Region", "Colombia", "Costa Rica", "Cuba", "Democratic People's Republic of Korea",
"Dominican Republic", "Ecuador", "El Salvador", "Guam", "Guatemala", "Honduras", "India",
"Indonesia", "Iran (Islamic Republic of)", "Iraq", "Israel", "Jamaica", "Japan", "Kuwait",
"Kyrgyzstan", "Lao People's Democratic Republic", "Lebanon", "Malaysia", "Mexico", "Mongolia",
"Myanmar", "Nepal", "New Zealand", "Oman", "Pakistan", "Paraguay", "Peru", "Philippines",
"Puerto Rico", "Qatar", "Republic of Korea", "Saudi Arabia", "Singapore", "Sri Lanka",
"State of Palestine", "Syriab Arab Republic", "Thailand", "United Arab Emirates",
"United States of America", "Uruguay", "Uzbekistan", "Venezuela (Bolivarian Republic of)",
"Viet Nam", "Taiwan")
World_Countries<- all_data %>% filter(Country_Name %in% World)#2 Graphs For X: cases per inhabitants and Y: CFR (Case Fatality Ratio)
#2.1 Graph for Europe
plot_cases_cfr_europe <- ggplot(European_Countries, aes(x = cases_per_100000inhabitants, y = case_fatality_ratio, label = Country_Name)) +
labs(x = "Cases Per 100,000 Inhabitants", y = "Case Fatality Ratio") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6) +
labs("Population Size") +
scale_size_continuous(name="Population\nSize", limits = c(38000, 150000000), breaks= c(50000, 1000000, 10000000, 100000000, 150000000), labels = c(50000, 1000000, 10000000, 100000000, 150000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_cases_cfr_europe## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_text_repel).
#ggsave("plot_cases_cfr_europe.pdf", plot = plot_cases_cfr_europe)
ggsave("new_plot_cases_cfr_europe.pdf", plot = plot_cases_cfr_europe, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).#2.2 Graph For World
plot_cases_cfr_world <- ggplot(World_Countries, aes(x = cases_per_100000inhabitants, y = case_fatality_ratio, label = Country_Name)) +
labs(x = "Cases Per 100,000 Inhabitants", y = "Case Fatality Ratio") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
scale_size_continuous(name="Population\nSize", limits=c(50000, 14500000000), breaks = c(50000, 100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_cases_cfr_world ## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_text_repel).
ggsave("new_plot_cases_cfr_world.pdf", plot = plot_cases_cfr_world, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_text_repel).#2.3 Graph for active variables for PCA
PCA_graph_data <- all_data %>% filter(Country_Name %in% (rownames(PCA_alldata)))
plot_cases_cfr_PCA <- ggplot(PCA_graph_data, aes(x = cases_per_100000inhabitants, y = case_fatality_ratio, label = Country_Name)) +
labs(x = "Cases Per 100,000 Inhabitants", y = "Case Fatality Ratio") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
scale_size_continuous(name="Population\nSize", limits=c(100000, 14500000000), breaks = c(100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_cases_cfr_PCA
ggsave("new_plot_cases_cfr_PCA.pdf", plot = plot_cases_cfr_PCA, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image#3 Graphs for X: percent of population and Y: percent of cases
#3.1 Construct new columns to get percent of world population
European_Countries <- European_Countries %>% mutate(percent_europe_pop = pop_2020/(sum(pop_2020, na.rm = TRUE)))
World_Countries <- World_Countries %>% mutate(percent_world_pop = pop_2020/(sum(pop_2020, na.rm = TRUE)))
PCA_graph_cases_pop <- PCA_graph_data %>% mutate(percent_PCA_pop = pop_2020/(sum(pop_2020, na.rm = TRUE)))
#3.2 Construct new columns to get percent of world cases for Europe and World
European_Countries <- European_Countries %>% mutate(percent_europe_case = total_cases/(sum(total_cases, na.rm = TRUE)))
World_Countries <- World_Countries %>% mutate(percent_world_case = total_cases/(sum(total_cases, na.rm = TRUE)))
PCA_graph_cases_pop <- PCA_graph_cases_pop %>% mutate(percent_PCA_case = total_cases/(sum(total_cases, na.rm = TRUE)))
#3.3 Make graphs for Europe and World with the percent of pop and percent of cases for Europe and World
plot_percent_cases_europe <- ggplot(European_Countries, aes(x = log(percent_europe_pop), y = log(percent_europe_case), label = Country_Name)) +
labs(x = "Percent of Total European Population", y = "Percent of Total European Cases") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
geom_abline(slope = 1) +
scale_size_continuous(name="Population\nSize", limits = c(38000, 150000000), breaks= c(50000, 1000000, 10000000, 100000000, 150000000), labels = c(50000, 1000000, 10000000, 100000000, 150000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) + theme(legend.spacing.x = unit(4.0, 'mm'))
plot_percent_cases_europe## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_text_repel).
ggsave("new_plot_percent_cases_europe.pdf", plot = plot_percent_cases_europe, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).plot_percent_cases_world <- ggplot(World_Countries, aes(x = log(percent_world_pop), y = log(percent_world_case), label = Country_Name)) +
labs(x = "Percent of Total World Population", y = "Percent of Total World Cases") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
geom_abline(slope = 1) +
scale_size_continuous(name="Population\nSize", limits=c(50000, 14500000000), breaks = c(50000, 100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_percent_cases_world## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_text_repel).
ggsave("new_plot_percent_cases_world.pdf", plot = plot_percent_cases_world, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_text_repel).#Do the percent of population vs percent of cases for the PCA active variables as well
plot_percent_cases_PCA <- ggplot(PCA_graph_cases_pop, aes(x = log(percent_PCA_pop), y = log(percent_PCA_case), label = Country_Name)) +
labs(x = "Percent of Total Population", y = "Percent of Total Cases") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
geom_abline(slope = 1) +
scale_size_continuous(name="Population\nSize", limits=c(100000, 14500000000), breaks = c(100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_percent_cases_PCA
ggsave("new_plot_percent_cases_PCA.pdf", plot = plot_percent_cases_PCA, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image#4 Graphs for X: percent of population and Y: percent of death
#4.1 Construct new columns to get percent of world deaths for Europe and World
European_Countries <- European_Countries %>% mutate(percent_europe_death = total_deaths/(sum(total_deaths, na.rm = TRUE)))
World_Countries <- World_Countries %>% mutate(percent_world_death = total_deaths/(sum(total_deaths, na.rm = TRUE)))
PCA_graph_deaths_pop <- PCA_graph_cases_pop %>% mutate(percent_PCA_death = total_deaths/(sum(total_deaths, na.rm = TRUE)))
#4.2 Make graphs for Europe and World with the percent of pop and percent of deaths for Europe and World
plot_percent_deaths_europe <- ggplot(European_Countries, aes(x = log(percent_europe_pop), y = log(percent_europe_death), label = Country_Name)) +
labs(x = "Percent of Total European Population", y = "Percent of Total European Death") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
geom_abline(slope = 1) +
scale_size_continuous(name="Population\nSize", limits = c(38000, 150000000), breaks= c(50000, 1000000, 10000000, 100000000, 150000000), labels = c(50000, 1000000, 10000000, 100000000, 150000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) + theme(legend.spacing.x = unit(4.0, 'mm'))
plot_percent_deaths_europe## Warning: Removed 1 rows containing missing values (geom_point).## Warning: Removed 1 rows containing missing values (geom_text_repel).
ggsave("new_plot_percent_deaths_europe.pdf", plot = plot_percent_deaths_europe, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image## Warning: Removed 1 rows containing missing values (geom_point).
## Warning: Removed 1 rows containing missing values (geom_text_repel).plot_percent_deaths_world <- ggplot(World_Countries, aes(x = log(percent_world_pop), y = log(percent_world_death), label = Country_Name)) +
labs(x = "Percent of Total World Population", y = "Percent of Total World Death") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
geom_abline(slope = 1) +
scale_size_continuous(name="Population\nSize", limits=c(50000, 14500000000), breaks = c(50000, 100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_percent_deaths_world ## Warning: Removed 4 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_text_repel).
ggsave("new_plot_percent_deaths_world.pdf", plot = plot_percent_deaths_world, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image## Warning: Removed 4 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_text_repel).plot_percent_deaths_PCA <- ggplot(PCA_graph_deaths_pop, aes(x = log(percent_PCA_pop), y = log(percent_PCA_death), label = Country_Name)) +
labs(x = "Percent of Total Population", y = "Percent of Total Death") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
geom_abline(slope = 1) +
scale_size_continuous(name="Population\nSize", limits=c(100000, 14500000000), breaks = c(100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_percent_deaths_PCA
ggsave("new_plot_percent_deaths_PCA.pdf", plot = plot_percent_deaths_PCA, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image#5 Graphs for X: date_max_daily_case_minus_max_string_index Y: cases_per_100000inhabitants
#5.1 Graph for Europe
plot_cases_dates_europe <- ggplot(European_Countries, aes(x = date_max_daily_case_minus_max_string_index, y = cases_per_100000inhabitants, label = Country_Name)) +
labs(x = "Days From Date of Maximum Stringency Index to Date of Maximum Daily Cases", y = "Cases Per 100,000 Inhabitants") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
scale_size_continuous(name="Population\nSize", limits = c(38000, 150000000), breaks= c(50000, 1000000, 10000000, 100000000, 150000000), labels = c(50000, 1000000, 10000000, 100000000, 150000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) + theme(legend.spacing.x = unit(4.0, 'mm'))
plot_cases_dates_europe## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_text_repel).
ggsave("new_plot_cases_dates_europe.pdf", plot = plot_cases_dates_europe, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image
## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_text_repel).#5.2 Graph for the World
plot_cases_dates_world <- ggplot(World_Countries, aes(x = date_max_daily_case_minus_max_string_index, y = cases_per_100000inhabitants, label = Country_Name)) +
labs(x = "Days From Date of Maximum Stringency Index to Date of Maximum Daily Cases", y = "Cases Per 100,000 Inhabitants") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
scale_size_continuous(name="Population\nSize", limits=c(50000, 14500000000), breaks = c(50000, 100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_cases_dates_world## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## Warning: Removed 8 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_text_repel).
ggsave("new_plot_cases_dates_world.pdf", plot = plot_cases_dates_world, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image
## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_text_repel).#5.3 Graph for the PCA active countries
plot_cases_dates_PCA <- ggplot(PCA_graph_data, aes(x = date_max_daily_case_minus_max_string_index, y = cases_per_100000inhabitants, label = Country_Name)) +
labs(x = "Days From Date of Maximum Stringency Index to Date of Maximum Daily Cases", y = "Cases Per 100,000 Inhabitants") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
scale_size_continuous(name="Population\nSize", limits=c(100000, 14500000000), breaks = c(100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_cases_dates_PCA## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.
ggsave("new_plot_cases_dates_PCA.pdf", plot = plot_cases_dates_PCA, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image
## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.#6 Graphs for X: date_max_daily_case_minus_max_string_index Y: deaths_per_100000inhabitants
#6.1 Graph for Europe
plot_deaths_dates_europe <- ggplot(European_Countries, aes(x = date_max_daily_case_minus_max_string_index, y = deaths_per_100000inhabitants, label = Country_Name)) +
labs(x = "Days From Date of Maximum Stringency Index to Date of Maximum Daily Cases", y = "Deaths Per 100,000 Inhabitants") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
scale_size_continuous(name="Population\nSize", limits = c(38000, 150000000), breaks= c(50000, 1000000, 10000000, 100000000, 150000000), labels = c(50000, 1000000, 10000000, 100000000, 150000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) + theme(legend.spacing.x = unit(4.0, 'mm'))
plot_deaths_dates_europe## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## Warning: Removed 5 rows containing missing values (geom_point).## Warning: Removed 5 rows containing missing values (geom_text_repel).
ggsave("new_plot_deaths_dates_europe.pdf", plot = plot_deaths_dates_europe, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image
## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## Warning: Removed 5 rows containing missing values (geom_point).
## Warning: Removed 5 rows containing missing values (geom_text_repel).#6.2 Graph for World
plot_deaths_dates_world <- ggplot(World_Countries, aes(x = date_max_daily_case_minus_max_string_index, y = deaths_per_100000inhabitants, label = Country_Name)) +
labs(x = "Days From Date of Maximum Stringency Index to Date of Maximum Daily Cases", y = "Deaths Per 100,000 Inhabitants") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
scale_size_continuous(name="Population\nSize", limits=c(50000, 14500000000), breaks = c(50000, 100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_deaths_dates_world## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## Warning: Removed 8 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_text_repel).
ggsave("new_plot_deaths_dates_world.pdf", plot = plot_deaths_dates_world, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image
## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.## Warning: Removed 8 rows containing missing values (geom_point).
## Warning: Removed 8 rows containing missing values (geom_text_repel).#6.3 Graph for PCA countries
plot_deaths_dates_PCA <- ggplot(PCA_graph_data, aes(x = date_max_daily_case_minus_max_string_index, y = deaths_per_100000inhabitants, label = Country_Name)) +
labs(x = "Days From Date of Maximum Stringency Index to Date of Maximum Daily Cases", y = "Deaths Per 100,000 Inhabitants") +
geom_point(aes(size = pop_2020), colour = "blue", alpha = 0.2) +
geom_text_repel(size = 1.6, segment.size = 0.1) +
scale_size_continuous(name="Population\nSize", limits=c(100000, 14500000000), breaks = c(100000, 10000000, 100000000, 1000000000), guide=guide_legend(title.theme = element_text(size=7,angle = 0), label.theme = element_text(size=7,angle = 0))) +
theme(legend.spacing.x = unit(4.0, 'mm'))
plot_deaths_dates_PCA## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.
ggsave("new_plot_deaths_dates_PCA.pdf", plot = plot_deaths_dates_PCA, scale = 3, limitsize = FALSE)## Saving 21 x 15 in image
## Don't know how to automatically pick scale for object of type difftime. Defaulting to continuous.